Rapid preliminary screening detection method for influenza virus

Abstract

The invention discloses an influenza virus rapid preliminary screening detection method, and relates to the technical field of biological detection, and the method comprises the following steps: collecting fluorescence signal data of a detected sample, constructing a fluorescence signal time curve, dividing the fluorescence signal time curve into a baseline segment, an amplification segment and a platform segment, and adopting a weighted sliding difference curvature method to detect the influenza virus. Calculating a local curvature change rate of each center time point of an amplification section, constructing a dynamic curvature judgment reference based on noise characteristic parameters, further obtaining an amplification trend judgment result Boolean value, and generating a final preliminary screening conclusion by calculating a fluorescence fluctuation proportion and a fluorescence slope mean value of fluorescence signal data and judging an amplification termination behavior state. According to the method, the dynamic curvature judgment reference is constructed based on the baseline segment signal variance value and the maximum jump amplitude value, so that the curvature judgment threshold value can be flexibly adjusted along with the background noise level, misjudgment caused by fixed parameters is avoided, and stable amplification trend judgment under various noise conditions is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of biological detection, in particular to a rapid screening detection method for influenza virus. BACKGROUND

[0002] Influenza virus is a kind of pathogen mainly transmitted through respiratory tract, and its rapid detection is of great significance for disease prevention, clinical diagnosis and epidemic trend monitoring. At present, the conventional nucleic acid amplification detection method has been widely used in virus detection field, and the real-time fluorescence monitoring technology is one of the core means. This kind of method dynamically collects the fluorescence signal in the amplification reaction process, combines the amplification curve slope or the fluorescence signal intensity change characteristics, judges whether the nucleic acid amplification reaction occurs, so as to realize the qualitative identification of virus nucleic acid. Because of its shorter detection time and higher sensitivity, this technology has become an important means of virus rapid screening and is widely used in clinical diagnosis.

[0003] However, in the actual detection process, the traditional curve analysis method based on fixed judgment parameters has certain limitations, mainly reflected in two aspects: first, when the sample fluorescence signal intensity is affected by background noise or random fluctuation, the fixed threshold judgment method is easy to be disturbed, which affects the reliability of amplification trend identification; second, the conventional method is insufficient in analyzing the segmented behavior characteristics, and cannot effectively reflect the dynamic change law of amplification segment and platform segment, which may reduce the discrimination accuracy of the real amplification state. SUMMARY

[0004] In view of the above existing problems, the present application is proposed.

[0005] Therefore, the present application provides a rapid screening detection method for influenza virus to solve the problems of existing influenza virus detection technology that the fixed judgment parameters are easy to be disturbed under noise condition and the structured analysis of amplification behavior is insufficient, resulting in reduced discrimination accuracy.

[0006] To solve the above technical problems, the present application provides the following technical scheme: In a first aspect, the present application provides a rapid screening detection method for influenza virus, comprising, acquiring fluorescence signal data of the sample of the individual to be tested, and constructing a fluorescence signal time curve; calculating the slope of each time point in the fluorescence signal time curve, and dividing the fluorescence signal time curve into baseline segment, amplification segment and platform segment; in the baseline segment, extracting noise characteristic parameters; in the amplification segment, using a weighted sliding differential curvature method to calculate the local curvature change rate of each center time point; A dynamic curvature judgment benchmark is constructed based on the noise characteristic parameters, and an amplification trend judgment result Boolean value is obtained according to the local curvature change rate and the dynamic curvature judgment benchmark; In the platform segment, the fluorescence fluctuation proportion and the fluorescence slope average of the fluorescence signal data are calculated, and the amplification termination behavior state is judged; The final preliminary screening conclusion is generated by parallel judgment according to the amplification trend judgment result Boolean value and the state of the amplification termination behavior.

[0007] As a preferred scheme of the influenza virus rapid preliminary screening detection method, the steps of collecting the fluorescence signal data of the sample of the individual to be detected and constructing the fluorescence signal time curve are as follows, The sample of the individual to be detected is collected, and a standardized lysis buffer is added to the sample of the individual to be detected for lysis treatment, and the nucleic acid sample liquid after lysis is transferred to an influenza virus detection reaction tube; The constant temperature amplification reagent and the fluorescent dye are added to the influenza virus detection reaction tube, the constant temperature amplification reaction is carried out, and the fluorescence signal data is collected in real time; The fluorescence signal time curve is constructed with time as the horizontal coordinate and the fluorescence signal intensity as the vertical coordinate.

[0008] As a preferred scheme of the influenza virus rapid preliminary screening detection method, the steps of calculating the slope of each time point in the fluorescence signal time curve, and dividing the fluorescence signal time curve into a baseline segment, an amplification segment and a platform segment are as follows, The slope of each time point in the fluorescence signal time curve is calculated according to the difference in fluorescence intensity between adjacent points in the fluorescence signal time curve; According to the slope of each time point in the fluorescence signal time curve, the time segmentation boundary is defined, and the fluorescence signal time curve is divided into a baseline segment, an amplification segment and a platform segment.

[0009] As a preferred scheme of the influenza virus rapid preliminary screening detection method, the steps of extracting the noise characteristic parameters in the baseline segment are as follows, The noise characteristic parameters include the average intensity value of the baseline segment fluorescence signal data, the baseline segment signal variance value and the baseline segment maximum jump amplitude value; The average intensity value of the baseline segment fluorescence signal data is calculated by a signal average value algorithm; The baseline segment signal variance value is calculated based on the average intensity value of the baseline segment fluorescence signal data by a signal variance algorithm; The difference values of the fluorescence signal intensities of all adjacent time points in the baseline segment are calculated and compared value by value, and the baseline segment maximum jump amplitude value is selected.

[0010] As a preferred scheme of the rapid preliminary screening detection method for the influenza virus provided in the application, in the amplification section, the weighted sliding differential curvature method is used to calculate the local curvature change rate of each central time point, and the steps are as follows, In the amplification section, the central time point sliding window is extracted; The second-order differential method is used to calculate the initial local curvature change rate of the central time point; The maximum value of the fluorescence signal intensity change between the central time point and the adjacent time points before and after the central time point is extracted to construct a disturbance correction factor, and the disturbance correction factor is normalized; Based on the normalized disturbance correction factor and the maximum jump amplitude value of the baseline section, a disturbance correction coefficient is constructed, and the weighted sliding differential curvature method is used to calculate the local curvature change rate of each central time point.

[0011] As a preferred scheme of the rapid preliminary screening detection method for the influenza virus provided in the application, in the amplification section, the weighted sliding differential curvature method is used to calculate the local curvature change rate of each central time point, and the steps are as follows, Based on the signal variance value of the baseline section and the maximum jump amplitude value of the baseline section, a dynamic curvature determination reference is constructed; According to the local curvature change rate of each time point in the amplification section and the dynamic curvature determination reference, intensity determination and trend determination are performed; When the intensity determination and the trend determination are both satisfied, it is determined that the amplification section has a credible amplification trend, and a Boolean value True of the amplification trend determination result is output; when the above conditions are not satisfied, it is determined that the amplification section does not have a credible amplification trend, and a Boolean value False of the amplification trend determination result is output.

[0012] As a preferred scheme of the rapid preliminary screening detection method for the influenza virus provided in the application, in the platform section, the fluorescence fluctuation proportion and the fluorescence slope average of the fluorescence signal data are calculated, and the amplification termination behavior state is determined, and the steps are as follows, The maximum value, the minimum value and the average value of the fluorescence signal data in the platform section are extracted; The fluorescence fluctuation amplitude of the fluorescence signal data is obtained by calculating the difference between the maximum value and the minimum value of the fluorescence signal data; Based on the fluorescence fluctuation amplitude of the fluorescence signal data and the average value of the fluorescence signal data, the fluorescence fluctuation proportion of the fluorescence signal data in the platform section is calculated; According to the slope of each continuous time point in the platform section, the fluorescence slope average of the fluorescence signal data is calculated; The stability of the amplification termination behavior state is determined by comparing the fluorescence fluctuation proportion and the fluorescence slope average of the fluorescence signal data with the fluorescence signal stability threshold, respectively; When the amplification termination behavior state is determined as the fuzzy band, the stability of the amplification termination behavior state is determined by boundary compensation based on the dynamic curvature determination criterion, and the maximum local second-order difference value of the platform segment is calculated to redetermine the stability of the amplification termination behavior state.

[0013] As a preferred solution of the influenza virus rapid preliminary screening detection method, the final preliminary screening conclusion is generated by parallel determination of the amplification trend determination result Boolean value and the state of the amplification termination behavior, and the steps are as follows, By determining the amplification trend of the amplification segment, the amplification trend determination result Boolean value and the stability of the amplification termination behavior state of the platform segment are determined in parallel, and a hierarchical parallel judgment logic is constructed. Based on the hierarchical parallel judgment logic, the final preliminary screening conclusion is generated.

[0014] In a second aspect, the present application provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and wherein the computer program is executed by the processor to implement any step of the influenza virus rapid preliminary screening detection method according to the first aspect of the present application.

[0015] In a third aspect, the present application provides a computer readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement any step of the influenza virus rapid preliminary screening detection method according to the first aspect of the present application.

[0016] The present application has the following beneficial effects: by using the weighted sliding differential curvature method in the amplification segment, the local curvature change rate of each center time point is calculated, and the dynamic trend of the amplification behavior is analyzed in detail; further, the dynamic curvature determination criterion is constructed based on the baseline segment signal variance value and the maximum jump amplitude value, so that the curvature determination threshold can be flexibly adjusted according to the background noise level, false judgments caused by fixed parameters are avoided, and stable amplification trend determination under various noise conditions is realized. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0018] Fig. 1 The flowchart of the influenza virus rapid preliminary screening detection method.

[0019] Fig. 2 The flowchart of the fluorescence signal curve segmentation.

[0020] Fig. 3 Flow chart for preliminary screening conclusion. DETAILED DESCRIPTION

[0021] In order to make the above objectives, features and advantages of the present application more apparent, the specific embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0022] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. However, it will be apparent to one skilled in the art that the present application can be practiced without the specific details given herein, that the present application can be practiced with other different ways, and that the present application is not limited to the specific embodiments disclosed herein.

[0023] Secondly, the "one embodiment" or "embodiment" referred to herein means that the specific features, structures or characteristics can be included in at least one implementation of the present application. The "in one embodiment" appearing in different places in the specification does not mean the same embodiment, nor is it an embodiment that is independent or alternative to other embodiments.

[0024] Reference Figs. 1-3 For one embodiment of the present application, the embodiment provides a rapid preliminary screening detection method for influenza virus, comprising the following steps: S1, collecting fluorescence signal data of a sample of a subject, and constructing a fluorescence signal time curve.

[0025] Collecting a sample of a subject, adding a standardized lysis buffer to the sample of the subject for lysis treatment, and transferring the lysis nucleic acid sample solution to an influenza virus detection reaction tube; It should be noted that the standardized lysis buffer comprises a protease, a non-ionic surfactant and an RNA protective agent.

[0026] Adding a constant temperature amplification reagent and a fluorescent dye to the influenza virus detection reaction tube, performing constant temperature amplification reaction, and collecting fluorescence signal data in real time; The fluorescence signal data includes: fluorescence signal intensity, time, fixed collection period, total volume of the constant temperature amplification reaction system, and temperature of the constant temperature amplification reaction. Taking time as the abscissa and fluorescence signal intensity as the ordinate, a fluorescence signal time curve is constructed.

[0027] Further, adding a constant temperature amplification reagent and a fluorescent dye to the influenza virus detection reaction tube, setting the total volume of the constant temperature amplification reaction system, setting the temperature of the constant temperature amplification reaction, and maintaining the constant temperature until the amplification is completed. The fluorescence signal data is collected in real time according to the fixed collection period during the constant temperature amplification reaction. The collected fluorescence signal data is taken as the abscissa and the fluorescence signal intensity as the ordinate, forming a fluorescence signal time curve.

[0028] It should be noted that the constant temperature amplification reaction reagent includes amplification primer, probe and enzyme mixture; the total volume of the constant temperature amplification reaction system is set according to the use instruction of the specific kit, and is usually between 50 μL and 100 μL; based on the optimal temperature of enzyme activity and reaction efficiency, 42°C is selected as the temperature of the constant temperature amplification reaction according to the appropriate temperature recommended in multiple literature and experimental data; the fixed period of acquisition is set to meet the balance between sampling accuracy and experimental efficiency, and is usually set to 30 seconds; the fluorescence curve is a complete data set of the change of fluorescence signal intensity with time, denoted as , ,… , wherein is the time stamp of each sampling point.

[0029] For example, 1 mL of nasopharyngeal swab sampling liquid of the subject is taken, 200 μL of standardized lysis buffer is added, and vortex oscillation is performed for 30 seconds to fully mix. The mixed solution is incubated in a 56°C metal bath for 15 minutes, and manually shaken once every 5 minutes during the incubation. After completion, it is transferred to a 95°C dry bath for heating for 5 minutes to inactivate protease, and immediately cooled in an ice bath for 2 minutes. Centrifugation is performed at 12,000 rpm at 4°C for 3 minutes, 150 μL of supernatant is aspirated and transferred to a 0.2 mL detection reaction tube preloaded with constant temperature amplification reaction reagent, and RNase-free water is added to make the total volume of the reaction system reach 50 μL. The reaction tube is placed in a 42°C constant temperature amplification instrument, and the fluorescence signal intensity acquisition is started immediately at an interval of every 30 seconds, and the monitoring is continued for 60 minutes, and the fluorescence signal time curve with time as the horizontal coordinate and the fluorescence signal intensity as the vertical coordinate is generated in real time.

[0030] S2, the slope of each time point in the fluorescence signal time curve is calculated, and the fluorescence signal time curve is divided into baseline segment, amplification segment and platform segment.

[0031] According to the difference in fluorescence intensity between adjacent points in the fluorescence signal time curve, the slope of each time point in the fluorescence signal time curve is calculated; It should be noted that the slope of each time point in the fluorescence signal time curve is calculated as follows: ; , wherein is the time point of the fluorescence signal time curve, is the time point of the fluorescence signal time curve, is the time point of the fluorescence signal time curve, is the slope of the time point of the fluorescence signal time curve, is the fluorescence signal intensity of the time point of the fluorescence signal time curve; ​​​the fluorescence signal intensity at the first time point of the fluorescence signal time curve, the fluorescence signal intensity at the first time point of the fluorescence signal time curve, the time interval between two time points of the fluorescence signal time curve.

[0032] According to the slope of each time point in the fluorescence signal time curve, the time segmentation boundary is defined, and the fluorescence signal time curve is divided into baseline segment, amplification segment and platform segment.

[0033] It should be noted that the baseline segment is the initial stage of the isothermal amplification reaction, the fluorescence signal intensity changes smoothly, the slope is close to zero, and the end time point of the baseline segment is defined as When the slope of the continuous five time points of the baseline segment is lower than the set baseline segment determination threshold, and the change amplitude of the fluorescence signal intensity is small, the fluctuation range of the fluorescence signal intensity is close to the background noise, and the fifth time point is the end time point of the baseline segment. ; The baseline segment starts from time point to ; The baseline segment determination threshold is set to 0.01 units / min based on the empirical observation of the initial fluorescence signal intensity change characteristics in the amplification process; The amplification segment is the stage of significant increase of the fluorescence signal, and the amplification of the viral nucleic acid starts and accelerates; the start of the amplification segment is triggered by the mutation of the slope of the time point of the baseline segment, when the slope of the time point continuously increases and exceeds the set amplification segment start threshold, at this time the isothermal amplification reaction starts to rise significantly, enters the amplification segment; the end of the amplification segment is marked by the slowing down of the change of the slope of the time point, and the end time point of the amplification segment is defined as , after the slope reaches the maximum value in the amplification segment, the first continuous three time points with significantly decreased slope and exceeding the decline amplitude threshold are set as the end point of the amplification segment, i.e. ; At this time, the rate of slope change decreases significantly, and the fluorescence signal time curve tends to be stable, which is marked as the termination of the amplification segment; The amplification segment starts from the next time point after time point to , and the slope is greater than the set amplification segment start threshold; The amplification segment start threshold is set according to historical slope data, usually set to 0.02 units / min, which meets the requirement of distinguishing the obvious growth stage in the amplification process without being affected by the background noise; the decline amplitude threshold is set according to historical isothermal amplification reaction data, with a value range of 45% to 55%; The platform segment is the stage of saturation of the fluorescence signal intensity in the amplification segment and termination of the reaction, and the change of the fluorescence signal intensity tends to be stable, with the slope close to zero; the end time point of the platform segment is defined as When the slope of the time point of the platform segment is less than the stable threshold of the platform segment for 5 consecutive time points, and the amplitude of the fluorescence signal intensity is less than the threshold of the fluorescence fluctuation range of the platform segment, the 5th time point of the 5 consecutive time points of the platform segment is ; The platform segment starts from the next time point after the end of the amplification segment, i.e. time point , and ends at ; According to the common data change speed of the platform period, the stable threshold of the platform segment is usually set to 0.01 units / minute, and according to the actual amplification data characteristics, the threshold of the fluorescence signal intensity fluctuation range of the platform segment is set to the maximum fluorescence signal intensity ± 5%; S3, in the baseline segment, extracting noise characteristic parameters.

[0034] The noise characteristic parameters include the average intensity value of the baseline segment fluorescence signal data, the baseline segment signal variance value and the baseline segment maximum jump amplitude value; The average intensity value of the baseline segment fluorescence signal data is calculated by the signal average value algorithm; It should be noted that the average intensity value expression of the fluorescence signal data in the baseline segment is: ; Wherein, is the total number of time points in the baseline segment, is the average intensity value of the fluorescence signal data in the baseline segment; The average intensity value of the fluorescence signal data represents the overall level of the fluorescence signal intensity in the constant temperature amplification reaction system; The average intensity value of the baseline segment fluorescence signal data is defined as the baseline segment signal variance value by the signal variance algorithm; The expression of calculating the baseline segment signal variance value by the signal variance algorithm is: ; Wherein, is the variance value of the baseline segment signal; The baseline segment signal variance value reflects the degree of fluorescence signal intensity fluctuation, and the greater the variance, the stronger the background noise fluctuation.

[0035] The difference values of the fluorescence signal intensity of all adjacent time points in the baseline segment are calculated and compared value by value, and the baseline segment maximum jump amplitude value is selected.

[0036] It should be noted that in the baseline segment, the fluorescence signal intensity should show a smooth change with time, but occasionally there will be a sudden jump. By calculating the difference values of the fluorescence signal intensity of all adjacent time points in the baseline segment and comparing value by value, the sudden fluctuation of the fluorescence signal intensity caused by the sudden jump is quantified, and the baseline segment maximum jump amplitude value is selected, the expression is: ; in, This represents the maximum jump amplitude value of the baseline segment. This is a function to find the maximum value. The maximum jump amplitude of the baseline segment reflects the change in fluorescence signal intensity over a short period of time.

[0037] S4. In the amplification segment, the weighted sliding differential curvature method is used to calculate the local curvature change rate at each central time point.

[0038] In the amplification phase, a sliding window at the center time point is extracted; Furthermore, in the amplification phase, a sliding window for the central time point is constructed by selecting any central time point and two adjacent time points before and after the central time point, and the fluorescence signal intensity corresponding to each time point in the sliding window is extracted.

[0039] The initial local curvature change rate at the central time point is calculated using the second-order difference method. It should be noted that within the sliding window at the center time point, the second-order difference method is used to calculate the initial local curvature change rate at the center time point, expressed as: ; in, The first in the amplified segment A central time point, The fluorescence signal intensity at the center time point in the amplification segment. The fluorescence signal intensity at the time point preceding the center time point in the amplification segment. The fluorescence signal intensity at the time point following the center time point in the amplification segment. The initial local curvature change rate, The sampling interval is defined as follows: The maximum value of the fluorescence signal intensity change between the center time point and the adjacent time points is extracted to construct the perturbation correction factor, and the perturbation correction factor is then normalized. It should be noted that the perturbation correction factor is constructed based on the maximum value of the fluorescence signal intensity change between the central time point and the adjacent time points before and after it, and its expression is: ; in, This is a perturbation correction factor, calculated only within the amplification segment and specifically for the center time point of the current amplification segment. This represents the jump amplitude between adjacent points before and after the central time point. It is a local dynamic parameter, and each time point in the amplification segment will have a corresponding one. , is a sequence shape variable, and is normalized after obtaining the perturbation correction factor.

[0040] The disturbance correction factor after normalization and the maximum jump amplitude value of the baseline segment are used to construct a disturbance correction coefficient. A weighted sliding differential curvature method is used to calculate the local curvature change rate of each central time point. It should be noted that the disturbance correction coefficient is constructed based on the normalized disturbance correction factor and the maximum jump amplitude value of the baseline segment, and the expression is: ; Among them, is the disturbance correction coefficient; The value range of is between 0 and 1. The closer the value is to 1, the higher the stability of the current time point. When is close to , tends to 0, indicating that the fluorescence signal intensity fluctuation of the time point is close to the maximum background noise, and the reliability decreases. When is much smaller than , tends to 1, indicating that the background interference of the current time point is weak.

[0041] Further, the expression of the weighted local curvature change rate is: ; Among them, represents the local curvature change rate.

[0042] S5, a dynamic curvature judgment criterion is constructed based on the noise feature parameters. According to the local curvature change rate and the dynamic curvature judgment criterion, the amplification trend judgment result Boolean value is obtained.

[0043] The dynamic curvature judgment criterion is constructed based on the baseline segment signal variance value and the maximum jump amplitude value of the baseline segment; It should be noted that the dynamic curvature judgment criterion is constructed based on the baseline segment signal variance value and the maximum jump amplitude value of the baseline segment, and the expression is: ; Among them, is the dynamic curvature judgment criterion, is the adjustment coefficient of the maximum jump amplitude value of the baseline segment, The value range of is: 0.1 to 5.0; is the adjustment coefficient of the baseline segment signal variance value, The value range of is: 0.1 to 5.0.

[0044] According to the local curvature change rate of each time point in the amplification segment and the dynamic curvature judgment criterion, the intensity judgment and trend judgment are performed; When the intensity determination and the trend determination are both satisfied, it is determined that the amplification segment has a credible amplification trend, and a Boolean value True is output as the amplification trend determination result. When the above conditions are not satisfied, it is determined that the amplification segment does not have a credible amplification trend, and a Boolean value False is output as the amplification trend determination result.

[0045] It should be noted that when the local curvature rate of change of the continuous time points is greater than the dynamic curvature determination reference, and the local curvature rate of change gradually increases, it is determined that the intensity determination is satisfied; The first-order difference value of the local curvature rate of change is calculated, and when the difference value of the local curvature rate of change gradually increases in the continuous time points, and the difference value of the local curvature rate of change is greater than the set curvature change increment threshold, it is determined that the trend determination is satisfied; The curvature change increment threshold is usually set to 0.005 units / minute according to historical local curvature rate of change data 2 .

[0046] S6, in the platform segment, the fluorescence fluctuation proportion and the fluorescence slope average of the fluorescence signal data are calculated, and the amplification termination behavior state is determined.

[0047] The maximum value, the minimum value and the average value of the fluorescence signal data in the platform segment are extracted; The fluorescence fluctuation amplitude of the fluorescence signal data is obtained by calculating the difference between the maximum value and the minimum value of the fluorescence signal data; Based on the fluorescence fluctuation amplitude of the fluorescence signal data and the average value of the fluorescence signal data, the fluorescence fluctuation proportion of the fluorescence signal data in the platform segment is calculated; It should be noted that the fluorescence fluctuation amplitude of the fluorescence signal data is obtained by calculating the difference between the maximum value and the minimum value of the fluorescence signal data, and the expression is: ; Wherein, is the platform segment, is the maximum value of the fluorescence signal intensity in the platform segment, is the minimum value of the fluorescence signal intensity in the platform segment, represents the fluorescence fluctuation amplitude of the fluorescence signal data; Based on the fluorescence fluctuation amplitude of the fluorescence signal data and the average value of the fluorescence signal data, the fluorescence fluctuation proportion of the fluorescence signal data in the platform segment is calculated, and the expression is: ; Wherein, is the fluorescence fluctuation proportion of the fluorescence signal data in the platform segment, is the average value of the fluorescence signal intensity in the platform segment.

[0048] According to the slope of each continuous time point in the platform segment, the fluorescence slope average of the fluorescence signal data is calculated. It should be noted that, based on the slope at each consecutive time point of the plateau segment, the expression for the mean fluorescence slope of the fluorescence signal data is as follows: ; ; in, For the z-th time point in the platform segment, The first in the platform segment At a certain point in time, The first in the platform segment The slope at each time point This represents the number of valid sampling points within the platform segment. The mean fluorescence slope of the fluorescence signal data; The stability of the amplification termination behavior state is determined by comparing the fluorescence fluctuation ratio and the mean fluorescence slope of the fluorescence signal data with the fluorescence signal stability threshold. The stability of the amplification termination behavior state includes "stable", "unstable" and "fuzzy band"; It should be noted that the fluorescence signal stability threshold includes the plateau segment fluctuation threshold and the plateau segment slope threshold. The fluorescence signal stability threshold is set based on the fluorescence fluctuation amplitude data of historical fluorescence signal data. Stability assessment criteria are classified according to the following rules: 1. The fluorescence fluctuation ratio of the fluorescence signal data in the plateau segment is less than the plateau segment fluctuation threshold. And the average slope is less than the slope threshold of the platform segment. The fluorescence signal in the plateau segment was determined to be stable, indicating that the isothermal amplification reaction was complete. Platform segment fluctuation threshold Typically set to 0.05; Plateau segment slope threshold It is usually set to 0.005; II. The fluorescence fluctuation ratio of the fluorescence signal data in the plateau segment is greater than the plateau segment fluctuation threshold. The fluorescence signal in the plateau segment was determined to be unstable, indicating that the isothermal amplification reaction was incomplete. Platform segment fluctuation threshold It is usually set to 0.07, and Less than ; 3. The average slope is greater than the slope threshold of the plateau segment. The fluorescence signal in the plateau segment was determined to be unstable, indicating that the isothermal amplification reaction was incomplete. Platform Slope Threshold It is usually set to 0.01, and Less than ; IV. Fluorescence fluctuation ratio of fluorescence signal data in the plateau segment between 0.5 and 1.5, and the average slope is between between 0.5 and 1.5, and the average slope is between between 0.5 and 1.5, and the average slope is between between 0.5 and 1.5, and the average slope is between When the amplification termination behavior state is determined as a fuzzy band, the stability of the amplification termination behavior state is determined based on the dynamic curvature determination criterion for boundary compensation, and the maximum local second-order difference value of the platform segment is calculated to re-determine the stability of the amplification termination behavior state.

[0049] It should be noted that the stability of the amplification termination behavior state is determined based on the dynamic curvature determination criterion for boundary compensation, and the maximum local second-order difference value of the platform segment is calculated as follows: ; wherein, is the maximum local second-order difference value of the platform segment; When , it indicates that the disturbance of the platform segment is lower than the background tolerance fluctuation, and it is determined as "stable", otherwise, the disturbance is too high, and it does not have the termination characteristics, and it is determined as "unstable".

[0050] S7, the final screening conclusion is generated by parallel determination of the amplification trend determination result Boolean value and the state of the amplification termination behavior.

[0051] By determining the amplification trend of the amplification segment, the amplification trend determination result Boolean value and the stability of the amplification termination behavior state of the platform segment are determined in parallel, and a hierarchical parallel judgment logic is constructed; Based on the hierarchical parallel judgment logic, the final screening conclusion is generated.

[0052] It should be noted that the hierarchical parallel judgment logic includes, parallel judgment one, the amplification trend determination result Boolean value is Ture, and the stability of the amplification termination behavior state is determined as "stable"; Parallel judgment two, the amplification trend determination result Boolean value is Ture, and the stability of the amplification termination behavior state is determined as "fuzzy band", and it is "stable" after the boundary compensation determination of the stability of the amplification termination behavior state; When the parallel judgment is parallel judgment one and parallel judgment two, the sample of the subject is determined as positive, and the initial screening result is generated as detecting influenza virus; Parallel judgment three, the amplification trend determination result is False, and the stability of the amplification termination behavior state is determined as "stable"; Parallel judgment four, when the amplification trend determination result is Ture, but the stability of the amplification termination behavior state is determined as "fuzzy band" and has not been determined as "stable" after the boundary compensation determination of the stability of the amplification termination behavior state. When the parallel judgment is parallel judgment three and parallel judgment four, the sample of the tested individual is determined to be negative, and the preliminary screening result is that no influenza virus is detected; Parallel judgment five, the amplification trend determination result Boolean value is True, and the stability judgment of the amplification termination behavior state is "unstable"; Parallel judgment six, the amplification trend determination result Boolean value is True, the stability judgment of the amplification termination behavior state is "ambiguous band", and after the boundary compensation determination of the stability of the amplification termination behavior state, it is "unstable"; When the parallel judgment is parallel judgment five and parallel judgment six, the sample of the tested individual is determined to be suspicious positive, and the preliminary screening result is that there may be influenza virus, which needs to be further detected and confirmed; Parallel judgment seven, the amplification trend determination result Boolean value is False, and the stability judgment of the amplification termination behavior state is "unstable"; Parallel judgment eight, the amplification trend determination result Boolean value is False, but the stability judgment of the amplification termination behavior state is "ambiguous band" and "unstable" after the boundary compensation determination of the stability of the amplification termination behavior state When the parallel judgment is parallel judgment seven and parallel judgment eight, the sample of the tested individual is determined to be suspicious negative, and the preliminary screening result is that there may be no influenza virus, which needs to be further detected and confirmed.

[0053] The embodiment also provides a computer device suitable for the case of the rapid preliminary screening detection method of influenza virus, including a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions to realize the rapid preliminary screening detection method of influenza virus proposed in the above embodiment.

[0054] The computer device can be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner. The wireless manner can be achieved through WIFI, operator network, NFC (near field communication) or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or a key, trackball or touchpad arranged on the shell of the computer device. In addition, the input device can be an external keyboard, touchpad or mouse, etc.

[0055] The embodiment also provides a storage medium, which stores a computer program, and the computer program is executed by a processor to implement the method for rapid preliminary screening detection of influenza virus according to the above embodiment. The storage medium can be implemented by any type of volatile or non-volatile storage devices or a combination thereof, such as a static random access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic storage, a flash memory, a magnetic disk or an optical disk.

[0056] To sum up, the application realizes fine-grained analysis of the dynamic trend of amplification by calculating the local curvature rate of change of each central time point by using the weighted sliding differential curvature method in the amplification segment; further, the dynamic curvature determination reference is constructed based on the signal variance value and the maximum jump amplitude value of the baseline segment, so that the curvature determination threshold can be flexibly adjusted according to the background noise level, false judgments caused by fixed parameters are avoided, and stable amplification trend determination under various noise conditions is realized.

[0057] It should be noted that the above embodiments are only used to illustrate the technical solutions of the application but not limit the application. Although the application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the application can be modified or replaced equivalently without departing from the spirit and scope of the technical solutions of the application, and all of them should be covered in the scope of the claims of the application.

Claims

1. A rapid screening method for influenza virus, characterized in that: The method comprises the following steps: ​ Collecting fluorescence signal data of a sample of a subject, and constructing a fluorescence signal time curve; Calculating the slope of each time point in the fluorescence signal time curve, and dividing the fluorescence signal time curve into a baseline segment, an amplification segment, and a platform segment; In the baseline segment, extracting noise characteristic parameters; In the amplification segment, using a weighted sliding differential curvature method to calculate the local curvature change rate of each central time point; Based on the noise characteristic parameters, constructing a dynamic curvature judgment benchmark, and according to the local curvature change rate and the dynamic curvature judgment benchmark, obtaining an amplification trend judgment result Boolean value; In the platform segment, calculating the fluorescence fluctuation proportion and the average fluorescence slope of the fluorescence signal data, and judging the amplification termination behavior state; According to the amplification trend judgment result Boolean value and the state of the amplification termination behavior, performing parallel judgment to generate a final preliminary screening conclusion.

2. The method for rapid screening of influenza virus according to claim 1, wherein: The step of collecting fluorescence signal data of a sample of a subject, and constructing a fluorescence signal time curve, comprises the following steps: Collecting a sample of a subject, adding a standardized lysis buffer to the sample of the subject for lysis treatment, and transferring the lysis nucleic acid sample liquid to an influenza virus detection reaction tube; Adding a constant temperature amplification reagent and a fluorescent dye to the influenza virus detection reaction tube, performing constant temperature amplification reaction, and collecting fluorescence signal data in real time; Taking time as the horizontal coordinate and fluorescence signal intensity as the vertical coordinate, constructing a fluorescence signal time curve.

3. The method for rapid screening of influenza virus according to claim 2, wherein: The step of calculating the slope of each time point in the fluorescence signal time curve, and dividing the fluorescence signal time curve into a baseline segment, an amplification segment, and a platform segment, comprises the following steps: According to the fluorescence intensity difference between adjacent points in the fluorescence signal time curve, calculating the slope of each time point in the fluorescence signal time curve; According to the slope of each time point in the fluorescence signal time curve, defining time segmentation boundaries, and dividing the fluorescence signal time curve into a baseline segment, an amplification segment, and a platform segment.

4. The method for rapid screening of influenza virus according to claim 3, wherein: The step of extracting noise characteristic parameters in the baseline segment comprises the following steps: The noise characteristic parameters comprise an average intensity value of the baseline segment fluorescence signal data, a baseline segment signal variance value, and a baseline segment maximum jump amplitude value; According to the average intensity value of the baseline segment fluorescence signal data, calculating the baseline segment signal variance value by a signal variance algorithm; Calculating the difference value of the fluorescence signal intensity of all adjacent time points in the baseline segment and performing value-by-value comparison to select the baseline segment maximum jump amplitude value. The step of using a weighted sliding differential curvature method to calculate the local curvature change rate of each central time point in the amplification segment comprises the following steps:

5. The method for rapid screening of influenza virus according to claim 4, wherein: In the amplification segment, extracting a central time point sliding window; Using a second-order differential method to calculate the initial local curvature change rate of the central time point; Extracting the maximum value of the fluorescence signal intensity change between the central time point and the adjacent time points before and after the central time point to construct a disturbance correction factor, and performing normalization processing on the disturbance correction factor; Based on the normalized disturbance correction factor and the baseline segment maximum jump amplitude value, constructing a disturbance correction coefficient, and using a weighted sliding differential curvature method to calculate the local curvature change rate of each central time point. ​ 6. The method for rapid primary screening of influenza virus according to claim 5, wherein: The dynamic curvature determination reference is constructed based on the noise feature parameter, and the amplification trend determination result Boolean value is obtained according to the local curvature change rate and the dynamic curvature determination reference, and the steps are as follows, The dynamic curvature determination reference is constructed based on the baseline segment signal variance value and the baseline segment maximum jump amplitude value; The intensity determination and the trend determination are performed according to the local curvature change rate of each time point in the amplification segment and the dynamic curvature determination reference; When the intensity determination and the trend determination are both satisfied, it is determined that the amplification segment has a credible amplification trend, and the amplification trend determination result Boolean value True is output; when the intensity determination and the trend determination are not satisfied, it is determined that the amplification segment does not have a credible amplification trend, and the amplification trend determination result Boolean value False is output.

7. The method of rapid screening test for influenza virus as claimed in claim 6, wherein: In the platform segment, the fluorescence fluctuation proportion and the fluorescence slope average of the fluorescence signal data are calculated, and the amplification termination behavior state is determined, and the steps are as follows, The maximum value, the minimum value and the average value of the fluorescence signal data in the platform segment are extracted; The fluorescence fluctuation amplitude of the fluorescence signal data is obtained by calculating the difference between the maximum value and the minimum value of the fluorescence signal data; The fluorescence fluctuation proportion of the platform segment fluorescence signal data is calculated based on the fluorescence fluctuation amplitude of the fluorescence signal data and the average value of the fluorescence signal data; The fluorescence slope average of the fluorescence signal data is calculated according to the slope of each continuous time point in the platform segment; The stability of the amplification termination behavior state is determined by comparing the fluorescence fluctuation proportion and the fluorescence slope average of the fluorescence signal data with the fluorescence signal stability threshold value respectively; When the amplification termination behavior state is determined as a fuzzy band, the stability of the amplification termination behavior state is determined based on the dynamic curvature determination reference, the maximum local second-order difference value of the platform segment is calculated, and the stability of the amplification termination behavior state is determined again.

8. The method of rapid screening test for influenza virus as claimed in claim 7, wherein: The final preliminary screening conclusion is generated by parallel determination according to the amplification trend determination result Boolean value and the state of the amplification termination behavior, and the steps are as follows, The amplification trend determination result Boolean value is obtained by determining the amplification trend of the amplification segment, and the stability of the amplification termination behavior state of the platform segment is determined in parallel, and a hierarchical parallel judgment logic is constructed; Based on the hierarchical parallel judgment logic, the final preliminary screening conclusion is generated. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is characterized in that: The processor executes the computer program to realize the steps of the influenza virus rapid preliminary screening detection method in any one of claims 1-8.

10. A computer readable storage medium having stored thereon a computer program, characterized in that: The computer program is executed by the processor to realize the steps of the influenza virus rapid preliminary screening detection method in any one of claims 1-8.

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