A rapid initial screening method for influenza virus

Abstract

This invention discloses a rapid initial screening method for influenza virus, belonging to the field of biodetection technology. The method includes: collecting fluorescence signal data from the tested sample; constructing a fluorescence signal time curve; dividing the fluorescence signal time curve into a baseline segment, an amplification segment, and a plateau segment; using a weighted moving differential curvature method to calculate the local curvature change rate at each central time point in the amplification segment; constructing a dynamic curvature judgment benchmark based on noise characteristic parameters; further obtaining a Boolean value for the amplification trend judgment result; and determining the amplification termination behavior state by calculating the fluorescence fluctuation ratio and the mean fluorescence slope of the fluorescence signal data, thus generating the final initial screening conclusion. This invention constructs a dynamic curvature judgment benchmark based on the baseline signal variance and the maximum jump amplitude, allowing the curvature judgment threshold to be flexibly adjusted according to the background noise level, avoiding misjudgments caused by fixed parameters, and achieving stable amplification trend judgment under various noise conditions.

Description

Technical Field

[0001] This invention relates to the field of biological detection technology, and in particular to a rapid initial screening method for influenza virus. Background Technology

[0002] Influenza viruses are a class of pathogens primarily transmitted through the respiratory tract, and rapid detection is crucial for disease prevention, clinical diagnosis, and epidemic trend monitoring. Currently, conventional nucleic acid amplification detection methods are widely used in virus detection, with real-time fluorescence monitoring technology being one of the core techniques. This type of method dynamically acquires fluorescence signals during the amplification reaction and, combined with the slope of the amplification curve or changes in fluorescence signal intensity, determines whether a nucleic acid amplification reaction has occurred, thereby achieving qualitative identification of viral nucleic acids. Due to its short detection time and high sensitivity, this technology has become an important means of rapid virus screening and is widely used in clinical diagnosis.

[0003] However, in actual testing, traditional curve analysis methods based on fixed judgment parameters have certain limitations, mainly in two aspects: First, when the sample fluorescence signal intensity is affected by background noise or random fluctuations, the fixed threshold judgment method is easily interfered with, affecting the reliability of amplification trend identification; Second, conventional methods do not make sufficient use of segmented behavioral features when analyzing amplification curves, and cannot effectively reflect the dynamic change law of amplification segment and plateau segment, which may reduce the accuracy of the discrimination of the true amplification state. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a rapid initial screening method for influenza virus to solve the problems of existing influenza virus detection technologies where fixed judgment parameters are easily interfered with under noisy conditions and where insufficient structured analysis of amplification behavior leads to reduced discrimination accuracy.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a rapid initial screening method for influenza virus, comprising,

[0008] Fluorescence signal data of individual samples were collected, and fluorescence signal time curves were constructed.

[0009] Calculate the slope of each time point in the fluorescence signal time curve, and divide the fluorescence signal time curve into the baseline segment, the amplification segment, and the plateau segment;

[0010] In the baseline segment, noise feature parameters are extracted;

[0011] In the amplification segment, the weighted sliding difference curvature method is used to calculate the local curvature change rate at each central time point;

[0012] A dynamic curvature determination benchmark is constructed based on noise characteristic parameters. Based on the local curvature change rate and the dynamic curvature determination benchmark, the Boolean value of the amplification trend determination result is obtained.

[0013] In the plateau segment, the fluorescence fluctuation ratio and mean fluorescence slope of the fluorescence signal data are calculated to determine the amplification termination behavior status.

[0014] The final preliminary screening conclusion is generated by performing parallel judgments based on the Boolean value of the amplification trend determination result and the status of the amplification termination behavior.

[0015] As a preferred embodiment of the rapid initial screening method for influenza virus described in this invention, the steps include: collecting fluorescence signal data from samples of the tested individuals and constructing a fluorescence signal-time curve, as follows.

[0016] Collect samples from the tested individuals and add standardized lysis buffer to the samples for lysis. Transfer the lysed nucleic acid sample solution to an influenza virus detection reaction tube.

[0017] An isothermal amplification reaction reagent and fluorescent dye were added to an influenza virus detection reaction tube to carry out an isothermal amplification reaction, and fluorescence signal data were collected in real time.

[0018] A fluorescence signal-time curve was constructed with time as the x-axis and fluorescence signal intensity as the y-axis.

[0019] As a preferred embodiment of the rapid initial screening method for influenza virus described in this invention, the following steps are taken: calculating the slope of the fluorescence signal time curve at each time point, and dividing the fluorescence signal time curve into a baseline segment, an amplification segment, and a plateau segment.

[0020] Calculate the slope of each time point in the fluorescence signal time curve based on the fluorescence intensity difference between adjacent points in the fluorescence signal time curve;

[0021] Based on the slope of each time point in the fluorescence signal time curve, the time segment boundaries are defined, and the fluorescence signal time curve is divided into the baseline segment, the amplification segment, and the plateau segment.

[0022] In a preferred embodiment of the rapid initial screening method for influenza virus described in this invention, noise feature parameters are extracted from the baseline segment, as follows:

[0023] Noise characteristic parameters include the average intensity value of the baseline fluorescence signal data, the baseline signal variance value, and the maximum jump amplitude value of the baseline segment;

[0024] The average intensity value of the baseline fluorescence signal data is calculated using a signal averaging algorithm.

[0025] Based on the average intensity value of the baseline fluorescence signal data, the baseline signal variance value is calculated using a signal variance algorithm.

[0026] Calculate the difference in fluorescence signal intensity at all adjacent time points in the baseline segment and compare them value by value, then select the maximum jump amplitude value in the baseline segment.

[0027] As a preferred embodiment of the rapid initial screening method for influenza virus described in this invention, in the amplification phase, the weighted moving differential curvature method is used to calculate the local curvature change rate at each central time point, as follows:

[0028] In the amplification phase, a sliding window at the center time point is extracted;

[0029] The initial local curvature change rate at the central time point is calculated using the second-order difference method.

[0030] The maximum value of fluorescence signal intensity change between the central time point and the adjacent time points is extracted to construct the perturbation correction factor, and the perturbation correction factor is normalized.

[0031] The perturbation correction coefficients are constructed based on the normalized perturbation correction factor and the maximum jump amplitude of the baseline segment. The weighted sliding difference curvature method is used to calculate the local curvature change rate at each center time point.

[0032] As a preferred embodiment of the rapid initial screening method for influenza virus described in this invention, the following steps are taken: A dynamic curvature determination benchmark is constructed based on noise characteristic parameters; and a Boolean value for amplification trend determination is obtained based on the local curvature change rate and the dynamic curvature determination benchmark.

[0033] A dynamic curvature determination benchmark is constructed based on the baseline segment signal variance and the maximum jump amplitude of the baseline segment.

[0034] Intensity and trend are determined based on the local curvature change rate and dynamic curvature determination benchmark at each time point within the amplification segment.

[0035] When both the intensity and trend conditions are met, the amplification segment is considered to have a credible amplification trend, and the amplification trend determination result is output as a Boolean value of True. If the above conditions are not met, the amplification segment is considered to have no credible amplification trend, and the amplification trend determination result is output as a Boolean value of False.

[0036] In a preferred embodiment of the rapid initial screening method for influenza virus described in this invention, the following steps are taken: During the plateau segment, the fluorescence fluctuation ratio and mean fluorescence slope of the fluorescence signal data are calculated to determine the amplification termination status.

[0037] Extract the maximum, minimum, and average values ​​of fluorescence signal data from the plateau segment;

[0038] The fluorescence fluctuation amplitude of the fluorescence signal data is obtained by calculating the difference between the maximum and minimum values ​​of the fluorescence signal data.

[0039] Based on the fluorescence fluctuation amplitude and the average value of the fluorescence signal data, the fluorescence fluctuation ratio of the platform segment fluorescence signal data is calculated.

[0040] Calculate the mean fluorescence slope of the fluorescence signal data based on the slope at each consecutive time point of the platform segment;

[0041] 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.

[0042] When the amplification termination behavior state is determined to be a fuzzy band, the stability of the amplification termination behavior state is determined by boundary compensation based on the dynamic curvature determination benchmark, and the stability of the amplification termination behavior state is re-determined by calculating the maximum local second-order difference value of the plateau segment.

[0043] As a preferred embodiment of the rapid initial screening method for influenza virus described in this invention, the following steps are taken: A final initial screening conclusion is generated by simultaneously determining the Boolean value of the amplification trend judgment result and the state of the amplification termination behavior.

[0044] By determining the amplification trend of the amplification segment, obtaining the Boolean value of the amplification trend determination result and the stability of the amplification termination behavior state of the plateau segment, a hierarchical parallel judgment logic is constructed.

[0045] Based on hierarchical parallel judgment logic, the final preliminary screening conclusion is generated.

[0046] In a second aspect, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein the computer program, when executed by the processor, implements any step of the rapid initial screening method for influenza virus as described in the first aspect of the present invention.

[0047] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the rapid initial screening method for influenza virus as described in the first aspect of the present invention.

[0048] The beneficial effects of this invention are as follows: by using the weighted sliding differential curvature method in the amplification segment to calculate the local curvature change rate at each central time point, fine-grained analysis of the dynamic trend of amplification behavior is achieved; furthermore, by constructing a dynamic curvature judgment benchmark based on the baseline signal variance and the maximum jump amplitude, the curvature judgment threshold can be flexibly adjusted according to the background noise level, avoiding misjudgment caused by fixed parameters, and achieving stable amplification trend judgment under various noise conditions. Attached Figure Description

[0049] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 This is a flowchart of a rapid initial screening method for influenza virus.

[0051] Figure 2 A flowchart for segmenting the fluorescence signal curve.

[0052] Figure 3 This is a flowchart for determining the initial screening results. Detailed Implementation

[0053] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0054] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0055] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0056] Reference Figures 1-3 This is one embodiment of the present invention, which provides a rapid initial screening method for influenza virus, comprising the following steps:

[0057] S1. Collect fluorescence signal data of the tested individual samples and construct fluorescence signal time curves.

[0058] Collect samples from the tested individuals and add standardized lysis buffer to the samples for lysis. Transfer the lysed nucleic acid sample solution to an influenza virus detection reaction tube.

[0059] It should be noted that the standardized lysis buffer contains protease, nonionic surfactant, and RNA protectant.

[0060] An isothermal amplification reaction reagent and fluorescent dye were added to an influenza virus detection reaction tube to carry out an isothermal amplification reaction, and fluorescence signal data were collected in real time.

[0061] Fluorescence signal data includes: fluorescence signal intensity, time, fixed acquisition period, total volume of the isothermal amplification reaction system, and temperature of the isothermal amplification reaction;

[0062] A fluorescence signal-time curve was constructed with time as the x-axis and fluorescence signal intensity as the y-axis.

[0063] Furthermore, isothermal amplification reaction reagents and fluorescent dyes are added to the influenza virus detection reaction tube. The total volume of the isothermal amplification reaction system is set, the temperature of the isothermal amplification reaction is set, and the constant temperature is maintained until the amplification is completed. During the isothermal amplification reaction, fluorescence signal data is collected in real time at a fixed collection period. The collected fluorescence signal data are plotted with time as the abscissa and fluorescence signal intensity as the ordinate to form a fluorescence signal time curve.

[0064] It should be noted that the isothermal amplification reaction reagent includes a mixture of amplification primers, probes, and enzymes; the total volume of the isothermal amplification reaction system should be set according to the specific kit instructions, typically between 50µL and 100µL; based on the optimal temperature for enzyme activity and reaction efficiency, and according to suitable temperatures recommended in multiple literatures and experimental data, 42°C is selected as the temperature for the isothermal amplification reaction; a fixed acquisition period is set to balance sampling accuracy and experimental efficiency, typically set to 30 seconds; the fluorescence curve is a complete dataset of fluorescence signal intensity changes over time, denoted as [missing information]. , , ..., ,in, Timestamp for each sampling point.

[0065] For example, take 1 mL of nasopharyngeal swab sample from the subject, add 200 μL of standardized lysis buffer, and vortex for 30 seconds to mix thoroughly. Incubate the mixture in a 56°C metal bath for 15 minutes, manually shaking once every 5 minutes. After incubation, transfer to a 95°C dry bath for 5 minutes to inactivate the protease, and immediately cool in an ice bath for 2 minutes. Centrifuge at 12,000 rpm for 3 minutes at 4°C, and transfer 150 μL of the supernatant to a 0.2 mL detection reaction tube pre-filled with isothermal amplification reaction reagent. Add RNase-free water to bring the total reaction volume to 50 μL. Place the reaction tube in a 42°C isothermal amplification instrument and immediately start acquiring fluorescence signal intensity every 30 seconds. Monitor continuously for 60 minutes and generate a real-time fluorescence signal time curve with time on the x-axis and fluorescence signal intensity on the y-axis.

[0066] S2. Calculate the slope of each time point in the fluorescence signal time curve, and divide the fluorescence signal time curve into the baseline segment, the amplification segment, and the plateau segment.

[0067] Calculate the slope of each time point in the fluorescence signal time curve based on the fluorescence intensity difference between adjacent points in the fluorescence signal time curve;

[0068] It should be noted that the expression for calculating the slope of each time point in the fluorescence signal time curve is as follows:

[0069] ;

[0070] in, The first of the fluorescence signal time curves At a certain point in time, The first of the fluorescence signal time curves At a certain point in time, The first of the fluorescence signal time curves The slope at each time point The first of the fluorescence signal time curves Fluorescence signal intensity at each time point; The first of the fluorescence signal time curves Fluorescence signal intensity at each time point The time interval is the time point between two points on the fluorescence signal time curve.

[0071] Based on the slope of each time point in the fluorescence signal time curve, the time segment boundaries are defined, and the fluorescence signal time curve is divided into the baseline segment, the amplification segment, and the plateau segment.

[0072] It should be noted that the baseline segment represents the initial stage of the isothermal amplification reaction, during which the fluorescence signal intensity changes steadily with a slope close to zero. The end point of the baseline segment is defined as... When the slope of the baseline segment is lower than the set baseline segment determination threshold for five consecutive time points, and the variation amplitude of the fluorescence signal intensity is small, with the fluorescence signal intensity fluctuation range close to the background noise, then the fifth time point is considered the baseline segment determination threshold. ;

[0073] The baseline segment is from the time point Beginning, to until;

[0074] The baseline threshold was set at 0.01 units / minute based on empirical observations of the initial fluorescence signal intensity changes during amplification.

[0075] The amplification phase is characterized by a significant increase in fluorescence signal, indicating the initiation and accelerated amplification of viral nucleic acid. The initiation of the amplification phase is triggered by a sudden change in the slope of the baseline time point; when the slope at that time point... As the amplification threshold is continuously increased and exceeds the set threshold for initiation, the isothermal amplification reaction begins to rise significantly, entering the amplification phase. The end of the amplification phase is determined by the decrease in the slope at each time point; the end time point of the amplification phase is defined as... After the slope reaches its maximum value within the amplification segment, if for the first time there are three consecutive time points where the slope significantly decreases and exceeds the decrease threshold, then the third time point in this consecutive decreasing segment is set as the end point of the amplification segment. When the rate of slope change decreases significantly and the fluorescence signal time curve becomes stable, it is marked as the termination of the amplification segment.

[0076] The amplified segment is from time point From the next point in time, until Until then, the slope is greater than the set amplification segment initiation threshold;

[0077] The amplification initiation threshold is set based on historical slope data, typically at 0.02 units / minute, to distinguish the distinct growth phases in the amplification process without being affected by background noise; the decline threshold is set based on historical isothermal amplification reaction data, with a value ranging from 45% to 55%.

[0078] The plateau segment is the stage where the fluorescence signal intensity of the amplification segment tends to saturate and the reaction terminates. The change in fluorescence signal intensity tends to be stable, and the slope is close to zero. The end time point of the plateau segment is defined as... When the slope of the plateau segment at any of the five consecutive time points is less than the plateau segment stability threshold, and the amplitude of the fluorescence signal intensity change is less than the plateau segment fluorescence fluctuation range threshold, then the fifth time point of the five consecutive time points of the plateau segment is... ;

[0079] The platform segment is the time point after the amplification segment ends. From the next point in time, until until;

[0080] Based on the common data change rate during the plateau period, the plateau stability threshold is usually set at 0.01 units / minute. Based on the actual amplification data characteristics, the plateau fluorescence signal intensity fluctuation range threshold is set at ±5% of the maximum fluorescence signal intensity.

[0081] S3. Extract noise feature parameters from the baseline segment.

[0082] Noise characteristic parameters include the average intensity value of the baseline fluorescence signal data, the baseline signal variance value, and the maximum jump amplitude value of the baseline segment;

[0083] The average intensity value of the baseline fluorescence signal data is calculated using a signal averaging algorithm.

[0084] It should be noted that the expression for calculating the average intensity value of fluorescence signal data within the baseline segment is as follows:

[0085] ;

[0086] in, This represents the total number of time points within the baseline segment. This represents the average intensity value of the fluorescence signal data within the baseline segment;

[0087] The average intensity value of the fluorescence signal data represents the overall level of fluorescence signal intensity in the isothermal amplification reaction system;

[0088] The average intensity value of the baseline fluorescence signal data is taken and defined as the baseline signal variance value using the signal variance algorithm.

[0089] The expression for calculating the baseline signal variance using the signal variance algorithm is as follows:

[0090] ;

[0091] in, This represents the variance of the baseline segment signal;

[0092] The baseline signal variance reflects the degree of fluctuation in fluorescence signal intensity; the larger the variance, the stronger the background noise fluctuation.

[0093] Calculate the difference in fluorescence signal intensity at all adjacent time points in the baseline segment and compare them value by value, then select the maximum jump amplitude value in the baseline segment.

[0094] It should be noted that within the baseline segment, the fluorescence signal intensity should exhibit a stable change over time, but occasional sudden jumps may occur. The sudden fluctuations in fluorescence signal intensity caused by these jumps are quantified by calculating and comparing the differences in fluorescence signal intensity at all adjacent time points within the baseline segment. The maximum jump amplitude value within the baseline segment is selected, expressed as:

[0095] ;

[0096] in, This represents the maximum jump amplitude value of the baseline segment. This is a function to find the maximum value.

[0097] The maximum jump amplitude of the baseline segment reflects the change in fluorescence signal intensity over a short period of time.

[0098] 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.

[0099] In the amplification phase, a sliding window at the center time point is extracted;

[0100] 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.

[0101] The initial local curvature change rate at the central time point is calculated using the second-order difference method.

[0102] 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:

[0103] ;

[0104] in, The first in the amplification 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.

[0105] 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:

[0106] ;

[0107] 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.

[0108] The perturbation correction coefficients are constructed based on the normalized perturbation correction factor and the maximum jump amplitude value of the baseline segment. The weighted sliding difference curvature method is used to calculate the local curvature change rate at each center time point.

[0109] It should be noted that the perturbation correction coefficient is constructed based on the normalized perturbation correction factor and the maximum jump amplitude value of the baseline segment, and the expression is as follows:

[0110] ;

[0111] in, This is the disturbance correction factor;

[0112] The value of is between 0 and 1. The closer the value is to 1, the higher the stability at the current time point. near hour, A value approaching 0 indicates that the fluorescence signal intensity fluctuation at this time point is close to the maximum background noise, resulting in decreased reliability. much smaller hour, A value approaching 1 indicates weak background interference at the current time point.

[0113] Furthermore, the weighted expression for the rate of change of local curvature is as follows:

[0114] ;

[0115] in, This indicates the rate of change of local curvature.

[0116] S5. Construct a dynamic curvature determination benchmark based on noise characteristic parameters, and obtain the Boolean value of the amplification trend determination result based on the local curvature change rate and the dynamic curvature determination benchmark.

[0117] A dynamic curvature determination benchmark is constructed based on the baseline segment signal variance and the maximum jump amplitude of the baseline segment.

[0118] It should be noted that the expression for the dynamic curvature determination criterion, based on the baseline signal variance and the maximum jump amplitude of the baseline segment, is as follows:

[0119] ;

[0120] in, As a benchmark for determining dynamic curvature, This is the adjustment factor for the maximum jump amplitude value of the baseline segment. The value range is from 0.1 to 5.0; This is the adjustment coefficient for the variance of the baseline signal. The value range is from 0.1 to 5.0.

[0121] Intensity and trend are determined based on the local curvature change rate and dynamic curvature determination benchmark at each time point within the amplification segment.

[0122] When both the intensity and trend conditions are met, the amplification segment is considered to have a credible amplification trend, and the amplification trend determination result is output as a Boolean value of True. If the above conditions are not met, the amplification segment is considered to have no credible amplification trend, and the amplification trend determination result is output as a Boolean value of False.

[0123] It should be noted that when there are at least 5 consecutive time points where the rate of change of local curvature is greater than the dynamic curvature judgment benchmark, and the rate of change of local curvature gradually increases, the strength judgment is deemed to be satisfied.

[0124] Calculate the first-order difference value of the local curvature change rate. If the difference of the local curvature change rate gradually increases in consecutive time points and the difference of the local curvature change rate is greater than the set curvature change increment threshold, the trend judgment is considered to be satisfied.

[0125] The threshold for the increment of curvature change is typically set to 0.005 units / minute based on historical local curvature change rate data. 2 .

[0126] S6. In the plateau segment, calculate the fluorescence fluctuation ratio and the mean fluorescence slope of the fluorescence signal data to determine the amplification termination behavior status.

[0127] Extract the maximum, minimum, and average values ​​of fluorescence signal data from the plateau segment;

[0128] The fluorescence fluctuation amplitude of the fluorescence signal data is obtained by calculating the difference between the maximum and minimum values ​​of the fluorescence signal data.

[0129] Based on the fluorescence fluctuation amplitude and the average value of the fluorescence signal data, the fluorescence fluctuation ratio of the platform segment fluorescence signal data is calculated.

[0130] It should be noted that the expression for obtaining the fluorescence fluctuation amplitude of the fluorescence signal data by calculating the difference between the maximum and minimum values ​​of the fluorescence signal data is as follows:

[0131] ;

[0132] in, For the platform segment, This represents the maximum fluorescence signal intensity in the plateau segment. This represents the minimum fluorescence signal intensity in the plateau segment. This indicates the fluorescence fluctuation amplitude of the fluorescence signal data;

[0133] Based on the fluorescence fluctuation amplitude and the average value of the fluorescence signal data, the expression for calculating the fluorescence fluctuation ratio of the plateau segment fluorescence signal data is as follows:

[0134] ;

[0135] in, The fluorescence fluctuation ratio of the platform segment fluorescence signal data. This represents the average fluorescence signal intensity of the plateau segment.

[0136] Calculate the mean fluorescence slope of the fluorescence signal data based on the slope at each consecutive time point of the platform segment;

[0137] 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:

[0138] ;

[0139] ;

[0140] 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;

[0141] 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.

[0142] The stability of the amplification termination behavior state includes "stable", "unstable" and "fuzzy band";

[0143] 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.

[0144] Stability assessment criteria are classified according to the following rules:

[0145] 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.

[0146] Platform segment fluctuation threshold Typically set to 0.05; Plateau segment slope threshold It is usually set to 0.005;

[0147] 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.

[0148] Platform segment fluctuation threshold It is usually set to 0.07, and Less than ;

[0149] 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.

[0150] Platform Slope Threshold It is usually set to 0.01, and Less than ;

[0151] IV. Fluorescence fluctuation ratio of fluorescence signal data in the plateau segment to Between, and the average slope is between to Between these points, the signal changes are not obvious, but it is still difficult to directly determine whether it is stable or unstable, and the fluorescence signal in the plateau segment is identified as a blurry band;

[0152] When the amplification termination behavior state is determined to be a fuzzy band, the stability of the amplification termination behavior state is determined by boundary compensation based on the dynamic curvature determination benchmark, and the stability of the amplification termination behavior state is re-determined by calculating the maximum local second-order difference value of the plateau segment.

[0153] It should be noted that, based on the dynamic curvature determination criterion, the stability of the amplification termination behavior state is determined by boundary compensation, and the expression for the maximum local second-order difference value of the plateau segment is calculated as follows:

[0154] ;

[0155] in, This represents the maximum local second-order difference value of the platform segment;

[0156] when If the disturbance is below the background tolerance fluctuation, it is judged as "stable". Otherwise, if the disturbance is too high and does not have termination characteristics, it is judged as "unstable".

[0157] S7. Based on the Boolean value of the amplification trend determination result and the status of the amplification termination behavior, perform parallel determination to generate the final preliminary screening conclusion.

[0158] By determining the amplification trend of the amplification segment, obtaining the Boolean value of the amplification trend determination result and the stability of the amplification termination behavior state of the plateau segment, a hierarchical parallel judgment logic is constructed.

[0159] Based on hierarchical parallel judgment logic, the final preliminary screening conclusion is generated.

[0160] It should be noted that the hierarchical parallel judgment logic includes: Parallel judgment one, the amplification trend judgment result is True, and the stability judgment of the amplification termination behavior state is "stable".

[0161] Parallel judgment two: the amplification trend judgment result is True, and the stability judgment of the amplification termination behavior state is "fuzzy band". After boundary compensation judgment of the stability of the amplification termination behavior state, it becomes "stable".

[0162] When the parallel judgment is either Parallel Judgment 1 or Parallel Judgment 2, the sample of the tested individual is identified as positive, and the initial screening result is generated as influenza virus detected.

[0163] Parallel judgment three: the amplification trend judgment result is False, and the stability judgment of the amplification termination behavior state is "stable";

[0164] Parallel Judgment 4: When the amplification trend judgment result is True, but the stability judgment of the amplification termination behavior state is "fuzzy band" and the boundary compensation judgment of the stability of the amplification termination behavior state is not performed, it is judged as "stable".

[0165] When the parallel judgment is either parallel judgment three or parallel judgment four, the sample of the tested individual is determined to be negative, and the initial screening result is generated as no influenza virus detected.

[0166] Parallel judgment five: the amplification trend judgment result is True (Boolean value), and the stability judgment of the amplification termination behavior state is "unstable".

[0167] Parallel judgment six: The Boolean value of the amplification trend judgment result is True, the stability judgment of the amplification termination behavior state is "fuzzy band", and after boundary compensation judgment of the stability of the amplification termination behavior state, it is "unstable".

[0168] 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 initial screening test result is generated as possibly containing influenza virus, which requires further testing for confirmation.

[0169] Parallel judgment seven: The amplification trend judgment result is False, and the stability judgment of the amplification termination behavior state is "unstable".

[0170] Parallel judgment eight: The amplification trend judgment result is False (Boolean value), but the stability judgment of the amplification termination behavior state is "fuzzy band," and after boundary compensation judgment of the stability of the amplification termination behavior state, it becomes "unstable."

[0171] When the parallel judgment is parallel judgment seven and parallel judgment eight, the sample of the tested individual is determined to be suspiciously negative, and the initial screening test result is generated as possibly not having influenza virus, requiring further testing for confirmation.

[0172] This embodiment also provides a computer device suitable for the rapid initial screening and 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 implement the rapid initial screening and detection method of influenza virus as proposed in the above embodiment.

[0173] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0174] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the rapid initial screening method for influenza virus as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0175] In summary, this invention achieves fine-grained analysis of the dynamic trend of amplification behavior by employing a weighted sliding differential curvature method in the amplification segment to calculate the local curvature change rate at each central time point; furthermore, by constructing a dynamic curvature judgment benchmark based on the baseline signal variance and the maximum jump amplitude, the curvature judgment threshold can be flexibly adjusted according to the background noise level, avoiding misjudgments caused by fixed parameters, and achieving stable amplification trend judgment under various noise conditions.

[0176] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A rapid initial screening method for influenza virus, characterized in that: include, Fluorescence signal data of individual samples were collected, and fluorescence signal time curves were constructed. Calculate the slope of each time point in the fluorescence signal time curve, and divide the fluorescence signal time curve into the baseline segment, the amplification segment, and the plateau segment; In the baseline segment, noise feature parameters are extracted; In the amplification segment, the weighted sliding difference curvature method is used to calculate the local curvature change rate at each central time point; A dynamic curvature determination benchmark is constructed based on noise characteristic parameters. Based on the local curvature change rate and the dynamic curvature determination benchmark, the Boolean value of the amplification trend determination result is obtained. In the plateau segment, the fluorescence fluctuation ratio and mean fluorescence slope of the fluorescence signal data are calculated to determine the amplification termination behavior status. The final preliminary screening conclusion is generated by performing parallel judgments based on the Boolean value of the amplification trend determination result and the status of the amplification termination behavior.

2. The rapid initial screening method for influenza virus as described in claim 1, characterized in that: The steps for collecting fluorescence signal data from the tested individual samples and constructing fluorescence signal time curves are as follows: Collect samples from the tested individuals and add standardized lysis buffer to the samples for lysis. Transfer the lysed nucleic acid sample solution to an influenza virus detection reaction tube. An isothermal amplification reaction reagent and fluorescent dye were added to an influenza virus detection reaction tube to carry out an isothermal amplification reaction, and fluorescence signal data were collected in real time. A fluorescence signal-time curve was constructed with time as the x-axis and fluorescence signal intensity as the y-axis.

3. The rapid initial screening method for influenza virus as described in claim 2, characterized in that: The steps for calculating the slope of the fluorescence signal time curve at each time point and dividing the fluorescence signal time curve into a baseline segment, an amplification segment, and a plateau segment are as follows. Calculate the slope of each time point in the fluorescence signal time curve based on the fluorescence intensity difference between adjacent points in the fluorescence signal time curve; Based on the slope of each time point in the fluorescence signal time curve, the time segment boundaries are defined, and the fluorescence signal time curve is divided into the baseline segment, the amplification segment, and the plateau segment.

4. The rapid initial screening method for influenza virus as described in claim 3, characterized in that: The steps for extracting noise feature parameters from the baseline segment are as follows: The noise characteristic parameters include the average intensity value of the baseline fluorescence signal data, the baseline signal variance value, and the maximum jump amplitude value of the baseline segment; The average intensity value of the baseline fluorescence signal data is calculated using a signal averaging algorithm. Based on the average intensity value of the baseline fluorescence signal data, the baseline signal variance value is calculated using a signal variance algorithm. Calculate the difference in fluorescence signal intensity at all adjacent time points in the baseline segment and compare them value by value, then select the maximum jump amplitude value in the baseline segment.

5. The rapid initial screening method for influenza virus as described in claim 4, characterized in that: In the amplification segment, the weighted sliding difference curvature method is used to calculate the local curvature change rate at each central time point. The steps are as follows. In the amplification phase, a sliding window at the center time point is extracted; The initial local curvature change rate at the central time point is calculated using the second-order difference method. The maximum value of fluorescence signal intensity change between the central time point and the adjacent time points is extracted to construct the perturbation correction factor, and the perturbation correction factor is normalized. The perturbation correction coefficients are constructed based on the normalized perturbation correction factor and the maximum jump amplitude of the baseline segment. The weighted sliding difference curvature method is used to calculate the local curvature change rate at each center time point.

6. The rapid initial screening method for influenza virus as described in claim 5, characterized in that: The process involves constructing a dynamic curvature determination benchmark based on noise characteristic parameters, and obtaining a Boolean value for the amplification trend determination result based on the local curvature change rate and the dynamic curvature determination benchmark. The steps are as follows: A dynamic curvature determination benchmark is constructed based on the baseline segment signal variance and the maximum jump amplitude of the baseline segment. Intensity and trend are determined based on the local curvature change rate and dynamic curvature determination benchmark at each time point within the amplification segment. When both the strength and trend criteria are met, the amplification segment is considered to have a credible amplification trend, and the amplification trend determination result is output as a Boolean value of True. When neither the strength nor the trend criteria are met, the amplification segment is considered to have no credible amplification trend, and the amplification trend determination result is output as a Boolean value of False.

7. The rapid initial screening method for influenza virus as described in claim 6, characterized in that: In the plateau segment, the fluorescence fluctuation ratio and mean fluorescence slope of the fluorescence signal data are calculated to determine the amplification termination status. The steps are as follows: Extract the maximum, minimum, and average values ​​of fluorescence signal data from the plateau segment; The fluorescence fluctuation amplitude of the fluorescence signal data is obtained by calculating the difference between the maximum and minimum values ​​of the fluorescence signal data. Based on the fluorescence fluctuation amplitude and the average value of the fluorescence signal data, the fluorescence fluctuation ratio of the platform segment fluorescence signal data is calculated. Calculate the mean fluorescence slope of the fluorescence signal data based on the slope at each consecutive time point of the platform segment; 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. When the amplification termination behavior state is determined to be a fuzzy band, the stability of the amplification termination behavior state is determined by boundary compensation based on the dynamic curvature determination benchmark, the maximum local second-order difference value of the plateau segment is calculated, and the stability of the amplification termination behavior state is re-determined.

8. The rapid initial screening method for influenza virus as described in claim 7, characterized in that: The process of performing parallel determination based on the Boolean value of the amplification trend judgment result and the status of amplification termination behavior to generate the final preliminary screening conclusion is as follows: By determining the amplification trend of the amplification segment, obtaining the Boolean value of the amplification trend determination result and the stability of the amplification termination behavior state of the plateau segment, a hierarchical parallel judgment logic is constructed. Based on 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, characterized in that: When the processor executes the computer program, it implements the steps of the rapid initial screening and detection method for influenza virus according to any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the rapid initial screening and detection method for influenza virus as described in any one of claims 1 to 8.

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