Method for correcting multiple interferences in analyte measurement

Abstract

A method for correcting interferents in a multi-component medium. The multi-component medium contains a target analyte and a plurality of interferents, and the target analyte is detected by means of a measurable signal S. The method comprises: establishing a correlation fitting curve between an interferent concentration and a measurement result bias by means of a single-factor experiment; then, on the basis of a single-factor fitting curve formula, designing a multi-factor interference experiment; correcting a test value back to a theoretical value by means of a correction formula, so as to obtain a multi-factor interference correction formula; and then, on the basis of the correction formula, correcting a test result of a clinical sample having a known interferent concentration, so as to reflect a true value of a target to be tested.

Description

Correction methods for multiple interferences in analyte determination Technical Field

[0001] This invention belongs to the field of chemical analysis, and particularly relates to a method for correcting multiple interferences in in vitro diagnostic analysis. Background Technology

[0002] In the fields of biology and medicine, it is frequently necessary to detect target analytes. For example, in cancer diagnosis, rapid and reliable methods are often required to detect the presence of tumor cells in a patient's body. The detection of target analytes typically involves measurable signals, such as absorbance at a specific wavelength, atomic emission or absorption spectra, fluorescence intensity, and electrical signals. In the analysis of in vitro samples, especially biological samples, due to their complex composition, the presence of certain substances can significantly affect the intensity of measurable signals, thus impacting the accuracy of detection. Therefore, they need to be removed beforehand, but this often affects the efficiency and cost of sample analysis.

[0003] Taking luciferase as an example, labeled luciferase emits light when it detects a target protein, using the light signal to indicate the presence and concentration of the target protein. Common luciferases include firefly luciferase (FLuc), which uses luciferin as a substrate; renal luciferase (RLuc), Gaussian luciferase (GLuc), and deep-sea shrimp luciferase (OLuc), which uses coelenterate as a substrate; and Nanolucase (NLuc). Complementary bioluminescence induced by luciferase structure has advantages such as high luminescence efficiency, short reaction time, high sensitivity, and wide linear range, showing promising application prospects in homogeneous immunoassay for in vitro diagnostics. However, luciferase often encounters severe matrix interference in the diagnosis of body fluid samples, especially interference from hemoglobin and bilirubin in blood samples. Luciferase activity decreases significantly after the addition of blood samples, with varying degrees of decrease across different samples. The degree of activity decrease is correlated with the concentration of hemoglobin and bilirubin in the sample, severely affecting the application of this enzyme in clinical diagnosis. The main reason for the significant decrease in luminescence activity of luciferase in blood samples is the absorption of light by hemoglobin and bilirubin. Because of these issues, the development of luciferase as an in vitro diagnostic enzyme has been slow.

[0004] In biochemical assays, bilirubin oxidase and potassium ferricyanide are commonly used to alter the absorption wavelength of bilirubin, thereby eliminating bilirubin interference (CN201310200574.9). However, these oxidizing substances significantly inhibit the activity of luciferase; and although these substances alter the absorption peak wavelength range of bilirubin, they still overlap with the emission wavelength range of luciferase, thus their interference elimination effect is very limited.

[0005] Magnetic microparticle chemiluminescence involves multiple washing steps, which can largely remove endogenous interfering substances in serum or plasma samples, such as bilirubin, hemoglobin, and endogenous lipids. Therefore, it can tolerate a certain level of interfering substances without requiring correction. However, high concentrations of hemoglobin (such as whole blood or samples containing red blood cells) often cause significant interference to magnetic microparticle chemiluminescence. This is because, on the one hand, whole blood or samples containing red blood cells are highly viscous, and the amount of interfering substances remaining after washing is unknown, making it difficult to quantify the degree of light absorption. On the other hand, hemoglobin also has peroxidase activity. For chemiluminescence using hydrogen peroxide as a substrate, including magnetic microparticle chemiluminescence and some photo-induced homogeneous chemiluminescence, the hemoglobin remaining in the reaction system catalyzes the substrate's luminescence through its peroxidase activity, making it difficult to solve the interference problem using correction methods.

[0006] Therefore, there is a need for analytical methods for analytes that can quickly and effectively eliminate interference from multiple factors without requiring complex purification, capture, and washing processes. Summary of the Invention

[0007] To address at least one of the aforementioned technical problems, this invention provides an analytical method for a target analyte in a multi-component medium. This method, by correcting for multiple interfering substances or factors present in a multi-component medium, such as a blood sample (including whole blood, serum, and plasma), can quickly and accurately obtain the true concentration of the target analyte.

[0008] Therefore, in a first aspect of the invention, a method for correcting interfering substances in a multi-component medium is provided, the multi-component medium comprising a target analyte and n interfering substances, n being selected from 1 to 5, for example 1, 2, 3, 4, or 5, wherein the target analyte is detected by a measurable signal S, the method comprising:

[0009] Obtain the IF of each of the n types of interfering substances. m Interference factor C IFm The relationship function between the parameter Y and the measurable signal S, where Y IFm =f m (C IFm ), m is selected from 1 to n; and

[0010] For all the n interfering substances, a correction function for the parameter Z of the measurable signal S is established, where Z is related to the concentration of the target analyte, and Z 校正 =F(Y) IF1 ,...,Y IFn )×Z 实测 +g=F(f1(C IF1 ),...,f n (C IFn ))×Z 实测+g, where Z 实测 Z represents the measured value of parameter Z. 校正 F(Y) represents the value of the corrected parameter Z. IF1 ,...,Y IFn ) represents n independent variables Y IF1 To Y IFn The function is denoted by g, where g is the correction coefficient.

[0011] In some implementations, the relational function Y IFm =f m (C IFm (Selected from C) IFm The functions are exponential, linear, logarithmic, polynomial, power functions, or any combination thereof, with polynomial functions, power functions, or combinations thereof being preferred.

[0012] In some implementations, F(Y) IF1 ,...,Y IFn The n independent variables Y IF1 To Y IFn Each of the single-variable functions in the equation is obtained by multiplying or dividing each other.

[0013] In some implementations, the independent variable Y IF1 To Y IFn The single-variable functions are independently selected from exponential functions, linear functions, logarithmic functions, polynomial functions, power functions, or any combination thereof, with polynomial functions, power functions, or combinations thereof being preferred.

[0014] In some embodiments, the measurable signal S is an optical signal or an electrical signal. Preferably, the measurable signal S is generated by the combination and / or reaction of one or more signal generating reagents with the target analyte.

[0015] In some embodiments, the signal-generating reagent is selected from luciferase and its corresponding substrate, or alkaline phosphatase and its corresponding substrate, or peroxidase and its corresponding substrate, or acridinium ester, or acridinium sulfonamide, or ruthenium tripyridine. In some embodiments, the luciferase is selected from firefly luciferase (FLuc) and / or renal luciferase (RLuc) and / or Gaussia luciferase (GLuc) and / or oplophorus luciferase (OLuc) and / or nanolucase (NLuc).

[0016] In some embodiments, parameter Y is the same as parameter Z. In some embodiments, parameter Y is different from parameter Z. In some embodiments, parameter Y is a relative value of parameter Z, which is the ratio of parameter Z to any specified value. Preferably, the specified value is the value of parameter Z when the interfering substance concentration is 0 or any value.

[0017] In some embodiments, the parameters Y and Z are each independently selected from absorbance, relative luminescence (RLU), current intensity, or radioactivity, or a relative value of any of the foregoing.

[0018] In some embodiments, the measurable signal S is fluorescence, bioluminescence, or chemiluminescence, and the parameters Y and Z are each independently selected from relative luminescence and relative luminescence values.

[0019] In some embodiments, the multi-component medium is selected from serum, plasma, blood (i.e., whole blood sample), urine, cerebrospinal fluid, pleural effusion, pulmonary lavage fluid, ascites, tissue fluid, or saliva.

[0020] In some embodiments, the n interfering substances are selected from bilirubin, hemoglobin, erythrocytes, triglycerides, albumin, or any combination thereof.

[0021] In some implementations, the interfering factor C IFm The concentration is selected from hematocrit or interfering substance concentration. The interfering substance concentration can be an absolute concentration, a jaundice index I, or a hemolysis index H, or a value linearly correlated with its concentration calculated from the absorbance at a specific wavelength of the sample (e.g., OD450-OD505 values ​​highly correlated with bilirubin concentration or jaundice index), as long as the units used in the univariate interference curve fitting are consistent with the units used in subsequent multivariate and clinical samples.

[0022] In some implementations, obtaining the relation function includes establishing the relation function and / or using an established relation function. Preferably, establishing the relation function includes: targeting the interfering IF. m Provide at least one set of standard samples, wherein the at least one set of standard samples comprises at least two standard samples, each standard sample comprising a target analyte having the same concentration and a sample selected from IF. m Interference concentration C in the standard concentration group IFm Interference IF m The at least two standard samples each have a different concentration of interfering substance C. IFm And wherein the concentration of the target analyte is not zero; obtain the parameter Y of the measurable signal S for each standard sample; use different functions to apply the parameters Y and C to all Y and C in the at least one set of standard samples.IFm Perform a fit; and select a coefficient of determination R that allows the fit to be performed. 2 Functions greater than 0.90 are used as the relational function Y. IFm =f m (C IFm ).

[0023] In some embodiments, establishing the correction function includes: providing at least one set of interfering samples comprising a target analyte and the n interfering substances, wherein the at least one set of interfering samples comprises at least two interfering samples, each interfering sample comprising a target analyte having the same concentration and the n interfering substances, the at least two interfering samples having different ratios of the n interfering substances, and wherein the concentration of the target analyte is not zero; obtaining the parameter Z of the measurable signal S of each interfering sample. 实测 ; and based on formula Z 校正 =F(Y) IF1 ,...,Y IFn )×Z 实测 +g, using different functions for all Z in the at least one interfering sample group 校正 Z 实测 and Y IFm Perform a fit; and select a coefficient of determination R that allows the fit to be performed. 2 A function greater than 0.90 is used as the correction function Z for the n interfering substances. 校正 =F(Y) IF1 ,...,Y IFn )×Z 实测 +g=F(f1(C IF1 ),...,f n (C IFn ))×Z 实测 +g.

[0024] In some embodiments, the ratio of the n interfering substances is a ratio of the interfering factors of the n interfering substances (e.g., a ratio of the same interfering factor or a ratio of different interfering factors, such as a concentration ratio, or a ratio of concentration and hematocrit), preferably an orthogonal ratio. In some embodiments, the concentrations of the n interfering substances contained in the first and / or second interfering samples are each independently selected from their corresponding IF values. m Standard concentration groups. The ranges of standard concentration groups for different interfering substances may be the same or different.

[0025] In some implementations, the coefficient of determination R for the fit of the relation function and / or the correction function 2Greater than 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, or 0.99 or any value in between.

[0026] In all embodiments, the n types of interfering objects include a first interfering object.

[0027] In some embodiments, obtaining the relationship function includes: providing a first set of standard samples for a first interfering substance, wherein the first set of standard samples comprises at least two first standard samples, each first standard sample comprising a target analyte having a first target analyte concentration and a first interfering substance concentration C selected from the IF1 standard concentration group. IF1 The first interfering substance IF1, wherein the at least two first standard samples have different concentrations of the first interfering substance C. IF1 And wherein the concentration of the first target analyte is not zero; obtain the parameter Y of the measurable signal S of each first standard sample; use different functions to apply the parameters Y and C to all Y and C in the first standard sample group. IF1 Perform a fit; and select a coefficient of determination R that allows the fit to be performed. 2 Functions greater than 0.90 are used as the relational function Y. IF1 =f1(C IF1 In some embodiments, obtaining the relationship function further includes: providing a second set of standard samples for the first interfering substance, wherein the second set of standard samples comprises at least two second standard samples, each second standard sample comprising a target analyte having a second target analyte concentration and a first interfering substance concentration C selected from the IF1 standard concentration group. IF1 The first interfering substance IF1, wherein the at least two second standard samples have different concentrations of the first interfering substance C. IF1 And wherein the concentration of the second target analyte is not zero and the concentration of the second target analyte is different from the concentration of the first target analyte; obtain the parameter Y of the measurable signal S of each second standard sample; use different functions to apply the parameters Y and C to all Y and C in the first standard sample group and the second standard sample group. IF1 Perform a fit; and select a coefficient of determination R that allows the fit to be performed. 2 Functions greater than 0.90 are used as the relational function Y. IF1 =f1(C IF1 ).

[0028] Those skilled in the art will understand that, for the same interfering substance (e.g., a first interfering substance), when using at least two sets of standard samples or interfering samples with different target analyte concentrations to establish a relationship function and / or a correction function with the same set of correction coefficients, different target analyte concentrations can use a relationship function with the same set of correction coefficients and / or a correction function with the same set of correction coefficients. The different target analyte concentrations can, in particular, correspond to the high and low values ​​of the target analyte concentration range that are likely to occur in actual detection, respectively. This allows the resulting correction function to correct for a wider or closer range of actual target analyte concentrations. Of course, those skilled in the art will understand that it is also possible to use only one set of standard samples or interfering samples to establish the relationship function and / or correction function. When using at least two sets of standard samples or interfering samples, it is preferable to fit all data in the set simultaneously, rather than fitting the data in one set separately.

[0029] When n=1, the first and / or second standard samples can correspond to the first and / or second interfering samples. Therefore, in some embodiments, the establishment of the correction function includes obtaining the parameter Z of the measurable signal S of each first standard sample and / or each second standard sample. 实测 Based on formula Z 校正 =F(Y) IF1 )×Z 实测 Using different functions for all Z in the first standard sample group and / or the second standard sample group 校正 Z 实测 and Y IF1 Perform a fit; and select a coefficient of determination R that allows the fit to be performed. 2 A function greater than 0.90 is used as the correction function Z for the interference. 校正 =F(Y) IF1 )×Z 实测 =F(f1(C) IF1 ))×Z 实测 .

[0030] In some implementations, n = 2.

[0031] Therefore, in some embodiments, obtaining the relationship function further includes: providing a third set of standard samples for the second interfering substance, wherein the third set of standard samples comprises at least two third standard samples, wherein each third standard sample comprises a target analyte having a third target analyte concentration and a second interfering substance concentration C selected from the IF2 standard concentration group. IF2 The second interfering substance IF2, wherein the at least two third standard samples each have a different concentration of the second interfering substance C. IF2And wherein the concentration of the third target analyte is not zero and the concentration of the third target analyte is the same as or different from the concentration of the first target analyte; obtain the parameter Y of the measurable signal S of each third standard sample; use different functions to apply the parameters Y and C to all Y and C in the third standard sample group. IF2 Perform a fit; and select a coefficient of determination R that allows the fit to be performed. 2 Functions greater than 0.90 are used as the relational function Y. IF2 =f2(C IF2 In some embodiments, obtaining the relationship function further includes: providing a fourth set of standard samples for the second interfering substance, wherein the fourth set of standard samples comprises at least two fourth standard samples, each fourth standard sample comprising a target analyte having a fourth target analyte concentration and a second interfering substance concentration C selected from the IF2 standard concentration group. IF2 The second interfering substance IF2, wherein the at least two fourth standard samples have different concentrations of the second interfering substance C. IF2 And wherein the concentration of the fourth target analyte is not zero and the concentration of the fourth target analyte is different from the concentration of the third target analyte; obtain the parameter Y of the measurable signal S of each fourth standard sample; use different functions to apply the parameters Y and C to all Y and C in the third standard sample group and the fourth standard sample group. IF2 Perform a fit; and select a coefficient of determination R that allows the fit to be performed. 2 Functions greater than 0.90 are used as the relational function Y. IF2 =f2(C IF2 ).

[0032] Accordingly, in some embodiments, establishing the correction function includes: providing a first and / or second set of interfering samples comprising a target analyte, first and second interfering substances, wherein the first set of interfering samples comprises at least two first interfering samples, each first interfering sample comprising a target analyte with the same concentration of a fifth target analyte and a first interfering substance IF1 and a second interfering substance IF2, the at least two first interfering samples having different ratios of first interfering substance IF1 and second interfering substance IF2; wherein the second set of interfering samples comprises at least two second interfering samples, each second dual interfering sample comprising a target analyte with the same concentration of a sixth target analyte and a first interfering substance IF1 and a second interfering substance IF2, the at least two second interfering samples having different ratios of first interfering substance IF1 and second interfering substance IF2; and wherein the concentrations of the fifth target analyte and the sixth target analyte are both non-zero and the concentration of the sixth target analyte is different from the concentration of the fifth target analyte; obtaining the parameter Z of the measurable signal S of each first interfering sample and / or each second interfering sample. 实测 Based on formula Z 校正=F(Y) IF1 ,Y IF2 )×Z 实测 +g, using different functions for all Z in the first interference sample group and / or the second interference sample group 校正 Z 实测 Y IF1 and Y IF2 Perform a fit; and select a coefficient of determination R that allows the fit to be performed. 2 A function greater than 0.90 is used as the correction function Z for the first and second interfering objects. 校正 =F(Y) IF1 ,Y IF2 )×Z 实测 +g=F(f1(C IF1 ),f2(C IF2 ))×Z 实测 +g.

[0033] In some embodiments, any two or three of the concentrations of the first, third, and fifth target analytes are the same or approximately the same. In some embodiments, any two or three of the concentrations of the second, fourth, and sixth target analytes are the same or approximately the same.

[0034] In some embodiments, the ratio of the first interfering substance IF1 to the second interfering substance IF2 is the concentration ratio of the first interfering substance IF1 to the second interfering substance IF2. In some embodiments, the ratio of the first interfering substance IF1 to the second interfering substance IF2 is a first interfering substance concentration C selected from the IF1 standard concentration group. IF1 The concentration C of the second interfering substance selected from the IF2 standard concentration group IF2 The orthogonal matching ratio.

[0035] In some implementations... or, Or, F(f1(C) IF1 ),f2(C IF2 ))=F1(f1(C IF1 ))×F2(f2(C IF2 )).

[0036] In some implementations, n = 3, and Or F1(f1(C) IF1 ))×F2(f2(C IF2 ))×F3(f3(C IF3 ))or or or or or

[0037] In some implementations, F1(f1(C) IF1 )) is f1(C IF1 The power function of ). In some implementations, F1(f1(C) IF1 f1(C) is f1(C) IF1 The exponential function of ). In some implementations, F1(f1(C) IF1 f1(C) is f1(C) IF1 A linear function of ). In some implementations, F1(f1(C) IF1 f1(C) is f1(C) IF1 The logarithmic function of ). In some implementations, F1(f1(C) IF1 f1(C) is f1(C) IF1 A polynomial function of ).

[0038] In some implementations, F2(f2(C) IF2 f2(C) is f2(C) IF2 The power function of ). In some implementations, F2(f2(C) IF2 f2(C) is f2(C) IF2 The exponential function of F2(f2(C). In some implementations, F2(f2(C) is... IF2 f2(C) is f2(C) IF2 A linear function of ). In some implementations, F2(f2(C) IF2 f2(C) is f2(C) IF2 The logarithmic function of ). In some implementations, F2(f2(C) IF2 f2(C) is f2(C) IF2 A polynomial function of ).

[0039] In some implementations, F3(f3(C) IF3 )) is f3(C IF3 The power function of ). In some implementations, F3(f3(C) IF3 )) is f3(C IF3 The exponential function of ). In some implementations, F3(f3(C) IF3 )) is f3(C IF3 A linear function of ). In some implementations, F3(f3(C) IF3 )) is f3(C IF3 The logarithmic function of F3(f3(C). In some implementations, F3(f3(C) is the logarithmic function of F3(f3(C)). IF3 )) is f3(C IF3 A polynomial function of ).

[0040] The method of this invention first establishes a correlation fitting curve between interfering factors, such as concentration, and measurement result bias of the interfering substance through single-factor experiments. Then, based on the above-mentioned single-factor fitting curve formula, a multi-factor interference experiment is designed. The test values ​​are corrected back to the theoretical values ​​through a correction formula, thereby obtaining the correction formula for multi-factor interference. Correcting the test results of clinical samples with known interfering substance concentrations according to the correction formula can reflect the true value of the target substance.

[0041] Therefore, in a second aspect of the present invention, a method for analyzing a target analyte in a multi-component medium is provided, the method comprising: correcting a parameter Z of a measurable signal S of the target analyte in the multi-component medium using a correction function obtained according to the method of the first aspect of the present invention.

[0042] In some embodiments, the analytical method further includes calculating the concentration of the target analyte using a calibrated parameter Z.

[0043] In some embodiments, the analysis method further includes: providing IF for each interfering substance in the multi-component medium. m Interference factor C IFm .

[0044] In a third aspect of the invention, a method for analyzing a target analyte in a multi-component medium is provided, the multi-component medium comprising the target analyte and n interfering substances, each interfering substance being IF m Contains interfering factor C IFm Where n is selected from 1 to 5 and m is selected from 1 to n; the target analyte can be detected by a measurable signal S, and the analysis method includes: using a correction function to measure the parameter Z of the measurable signal S of the multi-component medium. 实测 The correction is performed to obtain the corrected value of parameter Z. 校正 The correction function is defined as F × Z. 实测 +g, where F is a function of n interfering factors of the n interfering substances as variables and g represents the correction coefficient, and Z is related to the concentration of the target analyte.

[0045] In some embodiments, the analytical method further includes calculating the concentration of the target analyte using a calibrated parameter Z.

[0046] In embodiments of the present invention, F can be selected from any function, and g can be selected from any value.

[0047] In some embodiments, the measurable signal S is an optical signal or an electrical signal. Preferably, the measurable signal S is generated by the combination and / or reaction of one or more signal generating reagents with the target analyte.

[0048] In some embodiments, the parameter Z is selected from absorbance, relative luminescence (RLU), current intensity, or radioactivity, or a relative value of any of the foregoing.

[0049] In some embodiments, the multi-component medium is selected from serum, plasma, blood, urine, cerebrospinal fluid, pleural effusion, pulmonary lavage fluid, ascites, tissue fluid, or saliva.

[0050] In some embodiments, the n interfering substances are selected from bilirubin, hemoglobin, erythrocytes, triglycerides, albumin, or any combination thereof, and / or the interfering factor C IFm Selected from hematocrit or interferon concentration.

[0051] In a fourth aspect of the invention, a computer device is provided, comprising: one or more processors and a memory, the memory being used to store one or more programs; wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement an analysis method according to a second or third aspect of the invention.

[0052] In a fifth aspect of the invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the analysis method according to a second or third aspect of the invention. Attached Figure Description

[0053] Figure 1 shows the correspondence between serum index (jaundice index I) and plasma bilirubin (BR) concentration.

[0054] Figure 2 shows the correspondence between serum index (hemolysis index H) and plasma hemoglobin (HB) concentration.

[0055] Figure 3 shows the relationship between the PCT target concentration and the measured luminescence value in clinical plasma samples under the presence of two interfering factors.

[0056] Figure 4 shows the relationship between the PCT target concentration and the two-factor corrected luminescence value in clinical plasma samples.

[0057] Figure 5 shows the relationship between the PCT target concentration and the measured luminescence value in whole blood samples under the presence of two interfering factors.

[0058] Figure 6 shows the relationship between the PCT target concentration and the luminescence value after two-factor correction in whole blood samples.

[0059] Figure 7 shows the relationship between the PCT target concentration and the measured luminescence value in whole blood samples under the presence of two interfering factors.

[0060] Figure 8 shows the relationship between the PCT target concentration and the luminescence value after two-factor correction in whole blood samples.

[0061] Figure 9 shows the relationship between the PCT target concentration and the measured luminescence value in whole blood samples under the presence of two interfering factors.

[0062] Figure 10 shows the relationship between the PCT target concentration and the luminescence value after two-factor correction in whole blood samples. Detailed Implementation

[0063] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. The specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention in any way. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concepts of this disclosure. Such structures and techniques have also been described in many publications.

[0064] Unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as commonly used in the field to which this invention pertains. For the purposes of interpreting this specification, the following definitions will apply, and where appropriate, terms used in the singular will also include the plural forms, and vice versa.

[0065] Unless the context clearly indicates otherwise, the terms “a” and “an” as used herein include plural references.

[0066] In this disclosure, a "multi-component medium" refers to an aqueous or non-aqueous solution containing at least one target analyte. The multi-component medium may also contain other analytes or interfering components that may or may not interfere with the detection of the target analyte. Preferably, the multi-component medium of this invention refers to a biological fluid sample, including, for example, blood, urine, saliva, and derivatives thereof. Blood derivatives include, for example, serum, plasma, and artificial and / or synthetic blood.

[0067] "Analyte" refers to any substance or combination thereof found in a sample, the presence and / or amount of which is useful for detection. More specifically, it can be any medically important diagnostic biomarker, and non-limiting examples of such analytes include procalcitonin, glucose, creatinine, cholesterol, uric acid, methanol, ethanol, formaldehyde, glycerol-3-phosphate, etc. This invention does not limit the type of analyte, as long as it is a substance present in a multi-component medium and has medical analytical uses.

[0068] Depending on the type of target analyte and analytical method, the signal generating reagents of the present invention may include, for example, chromophores, luminescent complexes, enzymes and their substrates, such as 3,3',5,5'-tetramethylbenzidine, Trinder's reagent, glucose oxidase, alkaline phosphatase, peroxidase and oxygen acceptor (e.g., o-anisidine), acridine esters, ruthenium tripyridine; luciferases such as firefly luciferase (FLuc), renal luciferase (RLuc), Gaussia luciferase (GLuc), oplophorus luciferase (OLuc), and nanoluciferase (NLuc).

[0069] When a signal-generating reagent binds to or reacts with a target analyte, it can produce a measurable signal or event that is used to detect or quantify the target analyte. This measurable signal is typically a signal detectable using conventional methods, including absorbance, voltage, radioactivity, nuclear magnetic resonance, temperature, current, autoluminescence, and fluorescence.

[0070] In this disclosure, the term luciferase is a general term for enzymes in nature that can produce bioluminescence. This disclosure does not limit the type or source of the luciferase used, or the detection or analytical system using luciferase to determine a target analyte. Any luciferase known in the art, including secreted luciferases or intracellular luciferases, can be used in this invention. The term "secreted luciferase" refers to a luciferase that is secreted in its natural form from the cell where it is normally expressed to an extracellular location. The term "intracellular luciferase" refers to a luciferase that, when expressed, is retained in its natural form within the cell or cell membrane rather than secreted into an extracellular culture medium. A luciferase analytical system suitable for this invention can be a homogeneous immunoluminescence assay system comprising luciferase.

[0071] Those skilled in the art will understand that, in order to obtain the best calibration results, the same detection or analysis system can be used to obtain the measurable signal S when establishing the relational function, the calibration function, and finally calibrating the target analyte in the sample to be tested, for example, using the same detection or analysis reagents and / or detection instruments.

[0072] Different interfering substances have significantly different effects on different detection methods. For example, triglycerides significantly interfere with photochemiluminescence but have little effect on luciferase complementary luminescence. Bilirubin and hemoglobin significantly interfere with both methods. The interference mechanism can be divided into two basic types: one is interference caused by the interfering substance participating in a chemical reaction, such as the interference caused by hemoglobin catalyzing hydrogen peroxide; the other is interference caused by the interfering substance physically absorbing photons without participating in a chemical reaction, such as the interference caused by hemoglobin directly absorbing photons generated by chemiluminescence. Interference caused by participating in a chemical reaction is difficult to eliminate through correction due to the complexity of the reaction. Interference caused by physically absorbing photons, however, can be easily deduced by establishing a curve of interfering substance concentration versus light absorption.

[0073] The inventors discovered through research that for the luciferase complementation assay, interfering substances primarily cause interference through physical absorption of photons. These interfering substances do not participate in the chemical reaction. Therefore, it is easy to establish a curve relating the concentration of the interfering substance (or an indicator reflecting its concentration, such as absorbance at a characteristic absorption peak) to the luminescence signal. This curve can be used to deduce the correct luminescence signal of the interfering sample, thus obtaining accurate measurement results. Different interfering substances have varying degrees of absorption in luciferase-catalyzed luminescence. For example, bilirubin's absorption peak is around 450 nm, which overlaps with the emission peaks of Nanoluc or OLuc, resulting in significant interference. Hemoglobin is next, and blood lipids are the weakest. However, the inventors found that endogenous lipids have high absorbance values ​​across all wavelengths, yet their interference with luciferase RLU is the weakest. Therefore, directly correcting the result based solely on the degree of absorption of the interfering substance at a specific wavelength is not feasible. It is necessary to separately evaluate the impact of each endogenous interfering substance on the test deviation, fully consider the interactions between different endogenous interfering substances, and perform a comprehensive correction.

[0074] In this disclosure, "interfering substance," "interfering factor," or "interfering component" refers to a substance or factor that, in its presence, causes a significant enhancement or reduction in the measurable signal generated by the binding or reaction between the signal-generating reagent and the target analyte. It may affect the binding or reaction between the signal-generating reagent and the target analyte, or it may absorb a portion of the measurable signal.

[0075] In this disclosure, "approximately the same" means that they differ from each other by no more than 20%, 15%, 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, or 1% of their mean.

[0076] In this disclosure, "orthogonal matching" refers to different combinations of two or more independent variables obtained using the design concept of orthogonal experiments.

[0077] In this disclosure, "f(variable)" or "F(variable)" both represent a function of the variable in parentheses. The capitalization of the letter F or the number or designation following it are merely for distinguishing purposes and have no other limiting effect. Functions applicable to this invention include, but are not limited to, exponential functions, linear functions, logarithmic functions, polynomial functions, power functions, or any combination thereof.

[0078] The functions already conceived include, but are not limited to:

[0079] f(x) = A + Bx; f(x) = A + Be Cx f(x) = A + Bx + Cx 2 f(x) = A + Bx + Cx 2 +Dx 3 f(x) = A + Bx + Cx 2 +Dx 3 +...;f(x)=A(xB)C;

[0080] However, the present invention is not limited thereto, and any algorithm that can achieve accurate fitting and accurate correction is within the scope of this application.

[0081] This application uses Nanoluc luciferase as a reporter enzyme as an example to describe in detail a method for correcting interferences in blood samples (whole blood, serum, plasma). This method is applicable not only to interferences from bilirubin, hemoglobin, and hematocrit, but also to other interferences such as albumin and lipid interference. After correction, the true concentration data of the target substance can be obtained.

[0082] The inventors discovered through preliminary research that among common interfering substances in serum and plasma, hemoglobin and bilirubin are the most severe, while triglycerides have a relatively mild effect. In whole blood, hemoglobin and hematocrit are the most severe interfering substances, while triglycerides and bilirubin have relatively low interference. Therefore, in the following examples, hemoglobin and bilirubin are used as correction for interference in serum and plasma. In whole blood, due to the high concentration of hemoglobin, its inhibitory effect on activity far exceeds that of physiological concentrations of bilirubin; therefore, hemoglobin and hematocrit are used as correction for interference. Of course, correction for bilirubin can also be added.

[0083] The following examples first establish a correlation curve between interfering substance concentration and measurement result bias through single-factor experiments. Then, based on the formula for the single-factor fitting curve, a two-factor interference experiment is designed. The test values ​​are corrected back to the theoretical values ​​using a correction formula, thus obtaining the correction formula for two-factor interference. This method can significantly improve the clinical correlation of luciferase-based in vitro diagnostic reagents, overcome the interference of common interfering substances in blood samples such as hemoglobin, bilirubin, and hematocrit on test results, and make the detection results more accurate.

[0084] The following embodiments and accompanying drawings are provided to aid in understanding the present invention. However, it should be understood that these embodiments and drawings are for illustrative purposes only and do not constitute any limitation. The actual scope of protection of the present invention is set forth in the claims. It should be understood that any modifications and changes can be made without departing from the spirit of the present invention.

[0085] Example

[0086] Materials and methods

[0087] In all embodiments of this application, procalcitonin (PCT, S-3PCT1, Songtian Shengke) was used as the target. Hemoglobin was derived from human erythrocytes, prepared by ultrasonic lysis, dialyzed, concentrated, and then calibrated using a hematology analyzer (Sysmex, XN2000). Bilirubin was purchased from Aladdin (B104211), dissolved in dilute alkali, and then added. Serum, plasma, or simulated serum / plasma indices were measured using a coagulation analyzer (Sysmex, CS-5100).

[0088] This application uses a self-developed bioluminescent reagent specifically for detecting PCT as the detection reagent. Specifically, the PCT detection luminescent reagent contains reaction solution R1 with the following formulation: Tris: 2.42 g / L, MES: 7.81 g / L, KCl: 0.2 g / L, BSA: 15 g / L, PEG20000: 30 g / L, Triton X405: 100 μL / L, ProClin 300: 1 mL / L, DTT: 1 mM, pH 8.0. It also contains two fusion proteins: fusion protein 1 (Sequence ID NO. 01) is a fusion of SCFV antibody 1 and a large fragment of luciferase, and fusion protein 2 (Sequence ID NO. 02) is a fusion of SCFV antibody 2 and a small fragment of luciferase. In the presence of the PCT antigen, the SCFV on fusion protein 1 and fusion protein 2 binds to different epitopes of the same PCT antigen, bringing the large and small fragments of luciferase closer together to form a complementary complex. Upon addition of the luciferase substrate, bioluminescence is generated. Fusion proteins 1 and 2 were obtained through bacterial expression and nickel column purification, with concentrations in R1 ranging from 0.1 to 10 nM. The PCT detection luminescent reagent also includes substrate solution R2, with the following formulation: malonic acid: 12 mM, 2-hydroxypropyl β-cyclodextrin: 60 g / L, Tergitol 15s9: 400 μL / L, thiourea: 35 mM, coenzyme h: 150 μM, pH 3.0.

[0089] The testing method is as follows: Add 30 μl of reaction solution R1 to the wells of a white 96-well plate, add 5–20 μl of the sample to be tested, mix well, incubate at 37°C for 3–5 minutes, add 50 μL of substrate solution R2, and read the relative luminescence value (RLU) within 0–30 seconds using a bioluminescence detector (GloMax 96, Promega). The sample to be tested can be serum, plasma, whole blood, simulated plasma / serum, or simulated whole blood.

[0090] The simulated plasma / serum formulation is as follows: MES: 7.81 g / L, Tris: 2.42 g / L, mannitol: 50 g / L, BSA: 45 g / L, proclin 300: 2 mL / L, AntiFoam 204: 50 μL / L, pH 7.4. The simulated whole blood formulation is based on simulated plasma / serum with the addition of a certain amount of human hemoglobin (g / L). Verification has shown that the above-mentioned simulated plasma / serum and simulated whole blood are equivalent to real serum, plasma, and real whole blood, respectively, in the embodiments described in this application.

[0091] In the presence of the PCT target, this reagent induces luciferase complementation through ligand-mediated luminescence, resulting in luminescence upon substrate addition. The luminescence intensity is linearly correlated with the target concentration (or total amount). This reagent exhibits good sensitivity and linear range, and is a homogeneous detection reagent with rapid detection speed.

[0092] The inventors discovered that under different test conditions, such as changes in the volume of reaction solution R1, sample size, and substrate liquid volume R2, the form of the correction formula is not substantially affected, but the parameters of the correction formula will change significantly. Therefore, the same test conditions are used in all embodiments.

[0093] Fusion protein 1 (SEQ ID NO:01):

[0094] Fusion protein 2 (SEQ ID NO:02):

[0095] Example 1: Establishment of a one-factor correction formula for bilirubin (BR) in serum and plasma

[0096] According to Table 1, simulated serum / plasma samples containing different bilirubin concentrations (at least 4 concentrations, including 0 values, ranging from 0 to 30 mg / dL) and a certain concentration (one set of high concentration and one set of low concentration) of the target to be tested were artificially prepared.

[0097] Table 1 Sample preparation for single-factor correction of bilirubin in serum and plasma.

[0098] The relative luminescence (RLU) of each sample was obtained using the following testing method.

[0099] The testing method is as follows: 1. Add 30 μl of reaction solution R1 to the microplate containing the luminescence detection reagent; 2. Add 5 μl of the prepared simulated serum / plasma sample to the well containing the detection reagent; 3. Incubate the wells at 37℃ for 5 min; 4. Add 50 μl of substrate solution R2; 5. Detect the relative luminescence on a bioluminescence detector and record the luminescence value at a fixed time point (e.g., 4s) or a fixed time period (e.g., 4-30s) within the range of 0-30s, as the RLU. 实测 .

[0100] Each sample was tested twice to obtain RLU1. BR and RLU2 BR The average of these values ​​is taken as the RLU for each sample. BR实测 RLU of each sample BR实测 Divide by RLU when BR concentration is 0 respectively BR实测 To obtain the relative luminosity value (RLU) BR实测 For simplicity, Y will be used below. BR RLU BR %. The test results are shown in Table 2.

[0101] Table 2. Test results of univariate correction for bilirubin in serum and plasma.

[0102] Use a custom formula (I)

[0103] Where a, b, c, and d are all correction coefficients for this fitted curve, and are constants under specific test scenarios; Y corresponds to the first measured value of the measurable signal, and C... IF This corresponds to the concentration of a single interfering substance or other concentration-related parameters.

[0104] In this embodiment, Y corresponds to Y BR实测 C IF Corresponding to bilirubin concentration C BR Using the Solver function in Excel, Y... BR实测 With bilirubin concentration C BR Curve fitting was performed to obtain the formula for the single-factor fitting curve of bilirubin (I-1).

[0105] Alternatively, any other high-fit (R) method can be used. 2 The fitting formula is >0.90, as long as the fitted curve can simultaneously satisfy the correlation coefficient R of the high target concentration test group and the low target concentration test group. 2 A value of >0.90 is sufficient.

[0106] The correction results of the fitted curve formula were then verified. Yi was obtained at different bilirubin concentrations through calculation. BR校正 Values, using the RLU values ​​in Table 2. BR实测 Divide by the corresponding Y BR校正 Get RLU BR校正 The results are shown in Table 3. Validation revealed that the corrected luminescence values ​​(RLU) of different samples... BR校正 The coefficient of variation (CV) is less than 5%.

[0107] Results: Correcting the measured data using the bilirubin (BR) single-factor correction formula reduced the CV of the luminescence value from over 37% to 1.89%, demonstrating a good correction effect. The same set of correction coefficients can be used for both high and low target concentrations, indicating that the correction formula is not affected by the target concentration.

[0108] Table 3. Effects of univariate correction on serum and plasma bilirubin levels.

[0109] Example 2: Establishment of a one-factor correction formula for hemoglobin (HB) in serum and plasma

[0110] According to Table 4, simulated serum / plasma samples containing different hemoglobin concentrations (at least 4 concentrations, including 0 value, ranging from 0 to 20 g / L) and a certain concentration (one set of high concentration and one set of low concentration) of the target to be tested were artificially prepared.

[0111] Table 4 Sample preparation for single-factor correction of hemoglobin in serum and plasma.

[0112] The relative luminescence intensity (RLU) of each sample was obtained using a method similar to that in Example 1. Each sample was tested twice to obtain RLU1. HB and RLU2 HB The average of these values ​​is taken as the RLU for each sample. HB实测 RLU of each sample HB实测 Divide by RLU when HB concentration is 0 HB实测 The relative luminosity value Y2 was obtained. 实测 The test results are shown in Table 5.

[0113] Table 5. Test results of single-factor correction for hemoglobin in serum and plasma.

[0114] Using a data processing method similar to that in Example 1, the formula for the hemoglobin single-factor fitting curve (I-2) was obtained.

[0115] Alternatively, any other high-fit (R) method can be used. 2 The fitting formula is >0.90, as long as the fitted curve can simultaneously satisfy the correlation coefficient R of the high target concentration test group and the low target concentration test group. 2 A value of >0.90 is sufficient.

[0116] The correction results of the fitted curve formula were then verified. Yi was obtained at different bilirubin concentrations through calculation. HB校正 Values, using the RLU values ​​in Table 5. HB实测 Divide by the corresponding Y HB校正 Get RLU HB校正 The results are shown in Table 6. Validation revealed that the corrected luminescence values ​​(RLU) of different samples... HB校正 The coefficient of variation (CV) is less than 5%.

[0117] Results: Correcting the measured data using the hemoglobin (HB) single-factor correction formula reduced the coefficient of variation of luminescence values ​​from over 50% to below 2.1%, demonstrating good correction effectiveness. The same set of correction coefficients can be used for both high and low target concentrations, indicating that the correction formula is not affected by target concentration.

[0118] Table 6. Effects of univariate correction on serum and plasma hemoglobin levels.

[0119] Example 3: Establishment of a two-factor correction formula for bilirubin (BR) and hemoglobin (HB) in serum and plasma

[0120] Simulated plasma / serum samples with orthogonal ratios of different bilirubin concentrations (at least 3 concentrations, ranging from 0 to 30 mg / dL) and different hemoglobin concentrations (at least 4 concentrations, ranging from 0 to 20 g / L) were prepared according to Table 7. One group contained a higher concentration of the target to be tested, and the other group contained a lower concentration of the target to be tested.

[0121] Table 7 Sample preparation for two-factor correction of bilirubin and hemoglobin in serum and plasma.

[0122] The relative luminescence intensity (RLU) of each sample was obtained using a method similar to that in Example 1. Each dual-interference sample was tested twice to obtain RLU1. 双 and RLU2 双 The average of these values ​​is taken as the RLU for each double-interference sample. 双实测 (Z 实测 The test results are shown in Table 8.

[0123] Table 8. Test results of two-factor correction for bilirubin and hemoglobin in serum and plasma.

[0124] Use custom formula (II)Z 校正 =F(Y) IF1 ,Y IF2 )×Z 实测 +g, where Z 实测 Corresponding to the measurable signal value actually measured in the presence of interference, F(Y) IF1 ,Y IF2 ) represent the first interference Y IF1 Second interference Y IF2 The correction function. In this embodiment... Where Y BR Corresponding to the fitting formula in Example 1, Y HB Corresponding to the fitting formula in Example 2; e1, e2, and g are correction coefficients, which are all constants under specific test scenarios; the same set of correction coefficients is used for the high-concentration target group and the low-concentration target group.

[0125] Using the Solver function in Excel, the above variables are fitted to curves according to formula (II) to obtain the two-factor correction formula (II-1).

[0126] Alternatively, any other high-fit (R) method can be used. 2 The fitting formula is >0.90, as long as the fitted curve can simultaneously satisfy the correlation coefficient R of the high target concentration test group and the low target concentration test group. 2A value of >0.90 is sufficient.

[0127] The correction results of the fitted curve formula were then verified. Z-axis values ​​for different combinations of bilirubin and hemoglobin concentrations were calculated. 校正 The values ​​are shown in Table 9.

[0128] Results: Correcting the measured data using a two-factor correction formula for hemoglobin and bilirubin reduced the coefficient of variation (CV) of luminescence values ​​from over 27% to below 4.2%, demonstrating good correction effectiveness. The same set of correction coefficients can be used for both high and low target concentrations, indicating that the correction formula is unaffected by target concentration.

[0129] Table 9. Effects of two-factor correction on serum and plasma bilirubin and hemoglobin.

[0130] Example 4: Correspondence between serum indices and bilirubin and hemoglobin concentrations

[0131] Using simulated serum / plasma as a matrix, bilirubin was added to concentrations of 0, 0.5, 1.5, 2.5, 5, 7.5, 10, 12.5, 15, 20, 30, and 40 mg / dL, respectively, and hemoglobin was added to concentrations of 0.1, 0.15, 0.3, 0.5, 1, 2, 2.5, 3, 4, 6, 8, and 10 g / L, respectively.

[0132] The correlation curve between bilirubin concentration and jaundice index was established by measuring serum indices, including jaundice index I and hemolysis index H (Tables 10 and 11) (Figure 1, where the curve was fitted using Excel to obtain formula (III-1): C BR = -0.0291×I 2 +2.1101×I+1.4914, R 2 =0.9923), a correlation curve between hemoglobin concentration and hemolysis index was established (Figure 2, where the curve fitting was performed using Excel to obtain formula (III-2): C HB =0.0025×H 1.1598 R 2 =0.986), used to assign HB value to clinical samples with known hemolysis index H and BR value to clinical samples with known jaundice index I.

[0133] Table 10. Correspondence between serum index (jaundice index I) and plasma bilirubin (BR) concentration.

[0134] Table 11 Correspondence between serum index (hemolysis index H) and plasma hemoglobin (HB) concentration

[0135] Example 5: Calibration of luminescence values ​​measured in clinical serum and plasma samples.

[0136] Clinical plasma samples containing different concentrations of the target (containing different concentrations of bilirubin and hemoglobin) were collected. The jaundice index I and hemolysis index H were obtained. The bilirubin concentration C was calculated using fitting formulas III-1 and III-2 from Example 4. BR and hemoglobin concentration C HB The luminescence value (RLU) was tested using a luminescent reagent. 双实测 The corrected luminous value Z is calculated according to the correction formula (II-1) in Example 3. 校正 The results are shown in Table 12. Simultaneously, the PCT target concentration was used to adjust RLU levels. 双实测 and Z 校正 Plot the graphs and perform linear fitting to compare the correlation between target concentration and luminescence value before and after correction (Figures 3 and 4).

[0137] Results: After correcting for two-factor interference from bilirubin and hemoglobin in the samples, the correlation R of the clinical samples was [value missing]. 2 The value increased from 0.935 to 0.984, significantly eliminating the interference from both bilirubin and hemoglobin.

[0138] Table 12 Two-factor correction for bilirubin and hemoglobin in clinical plasma samples

[0139] Example 6: Establishment of a single-factor correction formula for hemoglobin in whole blood

[0140] According to Table 13, simulated whole blood samples containing different concentrations of human hemoglobin (at least 4 concentrations, including 125 g / L, ranging from 20 to 250 g / L) and a certain concentration (one high and one low concentration) of the target PCT were artificially prepared, with hematocrit (HCT) interference of 0.

[0141] Table 13 Sample preparation for single-factor correction of hemoglobin in whole blood.

[0142] The relative luminescence intensity (RLU) of each sample was obtained using a method similar to that in Example 2, and data processing was performed using a similar method, the only difference being that the RLU of each sample was... HB实测 Divide by the RLU when the HB concentration is 125 g / L respectively HB实测 The relative luminosity value Y3 was obtained. 实测 The test results and correction effects are shown in Table 14. The fitted curve formula (I-3) was obtained by fitting using formula (I).

[0143] Table 14 Test results and correction effects of one-way correction for hemoglobin in whole blood.

[0144] Results: Correcting the measured data using the hemoglobin-based single-factor correction formula reduced the coefficient of variation of luminescence values ​​from over 89.8% to below 4.22%, demonstrating good correction effectiveness. The same set of correction coefficients can be used for both high and low target concentrations, indicating that the correction formula is unaffected by target concentration.

[0145] Example 7: Establishment of a two-factor correction formula for hemoglobin (HB) and hematocrit in whole blood clinical samples

[0146] Clinical samples containing different target concentrations, hemoglobin concentrations, and hematocrits were collected (Table 15), and the relative luminescence intensity (RLU) of each sample was obtained according to the testing method in Example 1, which was used as the RLU. 双实测 (Z 实测 ).

[0147] Using the correction formula (II)Z 校正 =F(Y) IF1 ,Y IF2 )×Z 实测 +g is used to calibrate whole blood clinical samples. In this embodiment, Z 实测 Corresponding RLU 双实测 And Y HB The hemoglobin fitting formula (I-3) in Example 6 corresponds to the hematocrit correction formula (I-4) Y4=1-HCT / 100 (HCT is hematocrit, unit vol%); e3, e4, g, h, and k are correction coefficients, which are constants under specific test scenarios; the same set of correction coefficients are used for different concentrations of the target.

[0148] Using the Solver function in Excel, the above variables are fitted to curves according to formula (II) to obtain the two-factor correction formula (II-2).

[0149] Alternatively, any other high-fit (R) method can be used. 2 The fitting formula is >0.90, as long as the fitted curve can simultaneously satisfy the correlation coefficient R of the high target concentration test group and the low target concentration test group. 2 A value of >0.90 is sufficient.

[0150] The correction results of the fitted curve formula were then verified. Z-axis values ​​for different combinations of hemoglobin concentration and hematocrit were calculated. 校正 The values ​​are shown in Table 15.

[0151] Results: By correcting for both whole blood hemoglobin and hematocrit, the clinical correlation was significantly improved (Figures 5 and 6).

[0152] Table 15 Two-factor correction for whole blood hemoglobin and hematocrit

[0153] Example 8: Validation of the two-factor correction formula for hemoglobin (HB) and hematocrit in whole blood clinical samples

[0154] Clinical samples containing different target concentrations, hemoglobin concentrations, and hematocrits were collected (Table 16), and the relative luminescence intensity (RLU) of each sample was obtained according to the testing method in Example 1, which was used as the RLU. 双实测 (Z 实测 Note that the concentrations of fusion protein 1 and fusion protein 2 used in this batch of test reagent R1 are significantly lower than those in Example 1. Therefore, the measured RLU also shows an overall significant decrease compared to Example 7, but this does not affect the clinical relevance.

[0155] The clinical samples in this batch were corrected using the correction formula (II-2) obtained in Example 7.

[0156] Where Y HB The hemoglobin fitting formula (I-3) in Example 6 corresponds to the hematocrit correction formula (I-4) for Y4: Y4 = 1 - HCT / 100 (HCT is hematocrit, unit vol%).

[0157] Z was obtained by calculation for different combinations of hemoglobin concentration and hematocrit. 校正 The values ​​and results are shown in Table 16; the target concentration and Z value are compared. 校正 Correlation analysis was performed on the values, and the results are shown in Figure 7 (before correction) and Figure 8 (after correction).

[0158] Results: By correcting for both whole blood hemoglobin and hematocrit, the clinical correlation was significantly improved.

[0159] Table 16 Two-factor correction for whole blood hemoglobin and hematocrit

[0160] Example 9: Establishment and Validation of a Two-Factor Correction Formula for Hemoglobin (HB) and Hematocrit in Whole Blood Clinical Samples 2

[0161] Clinical samples containing different target concentrations, hemoglobin concentrations, and hematocrits were collected (Table 17), and the relative luminescence intensity (RLU) of each sample was obtained according to the testing method in Example 8, which was used as the RLU. 双实测 (Z 实测 ).

[0162] Using the correction formula (II)Z 校正 =F(Y) IF1 ,Y IF2 )×Z 实测 +g is used to calibrate whole blood clinical samples. In this embodiment, Z 实测 Corresponding RLU 双实测 And Y HB The hemoglobin fitting formula (I-3) in Example 6 corresponds to the hematocrit correction formula (I-4) Y4=1-HCT / 100 (HCT is hematocrit, unit vol%); e3, e4, g, h, and k are correction coefficients, which are constants under specific test scenarios; the same set of correction coefficients are used for different concentrations of the target.

[0163] Using the Solver function in Excel, the above variables are fitted to curves according to formula (II) to obtain the two-factor correction formula (II-3).

[0164] Alternatively, any other high-fit (R) method can be used. 2 The fitting formula is >0.90, as long as the fitted curve can simultaneously satisfy the correlation coefficient R of the high target concentration test group and the low target concentration test group. 2 A value of >0.90 is sufficient.

[0165] The correction results of the fitted curve formula were then verified. Z-axis values ​​for different combinations of hemoglobin concentration and hematocrit were calculated. 校正 The values ​​and results are shown in Table 17. The target concentration and Z... 校正 Correlation analysis was performed on the values, and the results are shown in Figure 9 (before correction) and Figure 10 (after correction).

[0166] Results: By correcting for both whole blood hemoglobin and hematocrit, the clinical correlation was significantly improved (Figures 9 and 10).

[0167] Table 17 Two-factor correction for whole blood hemoglobin and hematocrit

[0168] The technical solutions of the present invention are not limited to the specific embodiments described above. Any technical modifications made in accordance with the technical solutions of the present invention fall within the protection scope of the present invention.

Claims

1. A method for correcting interfering substances in a multi-component medium, wherein the multi-component medium comprises a target analyte and n interfering substances, n being selected from 1 to 5, the target analyte being detected by a measurable signal S, the method comprising: Obtain the IF of each of the n types of interfering substances. m Interference factor C IFm The relationship function between the parameter Y and the measurable signal S, where Y IFm =f m (C IFm ), m is selected from 1 to n; as well as For all the n interfering substances, a correction function for the parameter Z of the measurable signal S is established, where Z is related to the concentration of the target analyte, and Z 校正 =F(Y) IF1 ,...,Y IFn )×Z 实测 +g=F(f1(C IF1 ),...,f n (C IFn ))×Z 实测 +g, where Z 实测 Z represents the measured value of parameter Z. 校正 F(Y) represents the value of the corrected parameter Z. IF1 ,...,Y IFn ) represents n independent variables Y IF1 To Y IFn The function is denoted by g, where g is the correction coefficient.

2. The method according to claim 1, wherein the relational function Y IFm =f m (C IFm (Selected from C) IFm The exponential function, linear function, logarithmic function, polynomial function, power function, or any combination thereof; and / or F(Y IF1 ,...,Y IFn The n independent variables Y IF1 To Y IFn Each of the univariate functions in the equation is obtained by multiplying or dividing each other. Preferably, the independent variable Y is... IF1 To Y IFn The single-variable functions are selected independently from exponential functions, linear functions, logarithmic functions, polynomial functions, power functions, or any combination thereof.

3. The method according to claim 1 or 2, wherein the measurable signal S is an optical signal or an electrical signal, preferably, the measurable signal S is generated by the combination and / or reaction of one or more signal generating reagents with the target analyte.

4. The method according to claim 1 or 2, wherein the parameter Y and parameter Z are each independently selected from absorbance, relative luminescence (RLU), current intensity or radioactivity, or a relative value of any of the foregoing.

5. The method according to claim 1 or 2, wherein the multi-component medium is selected from serum, plasma, blood, urine, cerebrospinal fluid, pleural effusion, pulmonary lavage fluid, ascites, tissue fluid, or saliva.

6. The method according to claim 1 or 2, wherein the n interfering substances are selected from bilirubin, hemoglobin, erythrocytes, triglycerides, albumin, or any combination thereof, and / or The interfering factor C IFm Selected from hematocrit or interferon concentration.

7. The method according to claim 1 or 2, wherein obtaining the relation function includes establishing the relation function and / or using an established relation function, preferably, establishing the relation function includes: IF interference m Provide at least one set of standard samples, wherein the at least one set of standard samples comprises at least two standard samples, each standard sample comprising a target analyte having the same concentration and a sample selected from IF. m Interference concentration C in the standard concentration group IFm Interference IF m The at least two standard samples each have a different concentration of interfering substance C. IFm And the concentration of the target analyte is not zero; Obtain the parameter Y of the measurable signal S for each standard sample; Use different functions to evaluate all Y and C in the at least one standard sample group. IFm Perform fitting; and Choose a coefficient of determination R that allows the fit to be optimized. 2 Functions greater than 0.90 are used as the relational function Y. IFm =f m (C IFm ).

8. The method according to claim 1 or 2, wherein establishing the correction function comprises: Provide at least one interference sample group comprising a target analyte and the n interfering substances, wherein the at least one interference sample group comprises at least two interference samples, each interference sample comprising a target analyte having the same concentration and the n interfering substances, wherein the at least two interference samples have different ratios of the n interfering substances, and wherein the concentration of the target analyte is not zero; The parameter Z is obtained from the measurable signal S of each interfering sample. 实测 ; as well as Based on formula Z 校正 =F(Y) IF1 ,...,Y IFn )×Z 实测 +g, using different functions for all Z in the at least one interfering sample group 校正 Z 实测 and Y IFm Perform fitting; and Choose a coefficient of determination R that allows the fit to be optimized. 2 A function greater than 0.90 is used as the correction function Z for the n interfering substances. 校正 =F(Y) IF1 ,...,Y IFn )×Z 实测 +g=F(f1(C IF1 ),...,f n (C IFn ))×Z 实测 +g.

9. The method according to claim 8, wherein the ratio of the n interfering substances is the ratio of the interfering factors of the n interfering substances, preferably an orthogonal ratio, and even more preferably, the concentrations of the n interfering substances contained in the first and / or second interfering samples are each independently selected from their corresponding IF. m Standard concentration group.

10. The method of claim 7 or 8, wherein the coefficient of determination R for the fit of the relation function and / or the correction function 2 Greater than 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, or 0.99 or any value in between.

11. The method according to claim 1 or 2, wherein the n kinds of interfering substances include a first interfering substance.

12. The method according to claim 11, wherein obtaining the relational function comprises: For the first interfering substance, a first set of standard samples is provided, wherein the first set of standard samples comprises at least two first standard samples, each first standard sample comprising a target analyte having a concentration of a first target analyte and a first interfering substance having a concentration C selected from the IF1 standard concentration group. IF1 The first interfering substance IF1, wherein the at least two first standard samples have different concentrations of the first interfering substance C. IF1 , and wherein the concentration of the first target analyte is not zero; Obtain the parameter Y of the measurable signal S for each first standard sample; Use different functions to evaluate all Y and C in the first standard sample group. IF1 Perform fitting; and Choose a coefficient of determination R that allows the fit to be optimized. 2 Functions greater than 0.90 are used as the relational function Y. IF1 =f1(C IF1 ).

13. The method of claim 12, further comprising: For the first interfering substance, a second set of standard samples is provided, wherein the second set of standard samples comprises at least two second standard samples, each second standard sample comprising a target analyte having a concentration of the second target analyte and a first interfering substance having a concentration C selected from the IF1 standard concentration group. IF1 The first interfering substance IF1, wherein the at least two second standard samples have different concentrations of the first interfering substance C. IF1 And wherein the concentration of the second target analyte is not zero and the concentration of the second target analyte is different from the concentration of the first target analyte; Obtain the parameter Y of the measurable signal S for each second standard sample; Using different functions, apply all Y and C values ​​in the first and second standard sample groups. IF1 Perform fitting; and Choose a coefficient of determination R that allows the fit to be optimized. 2 Functions greater than 0.90 are used as the relational function Y. IF1 =f1(C IF1 ).

14. The method according to claim 13, wherein n = 1, and the establishment of the correction function includes: The parameter Z is obtained from the measurable signal S of each first standard sample and / or each second standard sample. 实测 ; Based on formula Z 校正 =F(Y) IF1 )×Z 实测 Using different functions for all Z in the first standard sample group and / or the second standard sample group 校正 Z 实测 and Y IF1 Perform fitting; and Choose a coefficient of determination R that allows the fit to be optimized. 2 A function greater than 0.90 is used as the correction function Z for the interference. 校正 =F(Y) IF1 )×Z 实测 =F(f1(C) IF1 ))×Z 实测 .

15. The method according to claim 11, wherein n = 2, and obtaining the relational function further includes: For the second interfering substance, a third set of standard samples is provided, wherein the third set of standard samples comprises at least two third standard samples, wherein each third standard sample comprises a target analyte having a concentration of the third target analyte and a second interfering substance having a concentration C selected from the IF2 standard concentration group. IF2 The second interfering substance IF2, wherein the at least two third standard samples each have a different concentration of the second interfering substance C. IF2 And wherein the concentration of the third target analyte is not zero and the concentration of the third target analyte is the same as or different from the concentration of the first target analyte; Obtain the parameter Y of the measurable signal S for each third standard sample; Using different functions, apply the values ​​of Y and C to all Y and C in the third standard sample group. IF2 Perform fitting; and Choose a coefficient of determination R that allows the fit to be optimized. 2 Functions greater than 0.90 are used as the relational function Y. IF2 =f2(C IF2 ).

16. The method of claim 15, further comprising: For the second interfering substance, a fourth set of standard samples is provided, wherein the fourth set of standard samples comprises at least two fourth standard samples, each fourth standard sample comprising a target analyte having a fourth target analyte concentration and a second interfering substance concentration C selected from the IF2 standard concentration group. IF2 The second interfering substance IF2, wherein the at least two fourth standard samples have different concentrations of the second interfering substance C. IF2 And wherein the concentration of the fourth target analyte is not zero and the concentration of the fourth target analyte is different from the concentration of the third target analyte; Obtain the parameter Y of the measurable signal S for each fourth standard sample; Different functions were used to evaluate all Y and C values ​​in the third and fourth standard sample groups. IF2 Perform fitting; and Choose a coefficient of determination R that allows the fit to be optimized. 2 Functions greater than 0.90 are used as the relational function Y. IF2 =f2(C IF2 ).

17. The method of claim 15, wherein establishing the correction function comprises: Provides a first and / or second interference sample group comprising a target analyte, first and second interfering substances, wherein the first interference sample group comprises at least two first interference samples, each first interference sample comprising a target analyte and a first interfering substance IF1 and a second interfering substance IF2 having the same concentration of a fifth target analyte, the at least two first interference samples having different ratios of first interfering substance IF1 and second interfering substance IF2, wherein the second interference sample group comprises at least two second interference samples, each second double interference sample comprising a target analyte and a first interfering substance IF1 and a second interfering substance IF2 having the same concentration of a sixth target analyte, the at least two second interference samples having different ratios of first interfering substance IF1 and second interfering substance IF2, and wherein the concentrations of the fifth target analyte and the sixth target analyte are both non-zero and the concentration of the sixth target analyte is different from the concentration of the fifth target analyte; The parameter Z is obtained from the measurable signal S of each first interfering sample and / or each second interfering sample. 实测 ; Based on formula Z 校正 =F(Y) IF1 ,Y IF2 )×Z 实测 +g, using different functions for all Z in the first interference sample group and / or the second interference sample group 校正 Z 实测 Y IF1 and Y IF2 Perform fitting; and Choose a coefficient of determination R that allows the fit to be optimized. 2 A function greater than 0.90 is used as the correction function Z for the first and second interfering objects. 校正 =F(Y) IF1 ,Y IF2 )×Z 实测 +g=F(f1(C IF1 ),f2(C IF2 ))×Z 实测 +g.

18. The method according to any one of the preceding claims, wherein any two or three of the concentrations of the first, third, and fifth target analytes are the same or approximately the same, and / or any two or three of the concentrations of the second, fourth, and sixth target analytes are the same or approximately the same.

19. The method according to claim 16, wherein the ratio of the first interfering substance IF1 and the second interfering substance IF2 is the concentration ratio of the first interfering substance IF1 and the second interfering substance IF2, preferably an orthogonal ratio of concentrations.

20. The method of claim 15, wherein or, Or, F(f1(C) IF1 ),f2(C IF2 ))=F1(f1(C IF1 ))×F2(f2(C IF2 )).

21. A method for analyzing a target analyte in a multi-component medium, the method comprising: The parameter Z of the measurable signal S of the target analyte in the multi-component medium is corrected using a correction function obtained by the method according to any one of the preceding claims, and optionally, the concentration of the target analyte is calculated from the corrected parameter Z.

22. A computer device, comprising: One or more processors and a memory, the memory being used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the analysis method according to claim 21.

23. A computer-readable storage medium having a computer program stored thereon, the computer program implementing the analysis method according to claim 21 when executed by a processor.

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