Method for identification of slow-growing bacteria

Abstract

The application discloses a method for identifying slow-growing bacteria, comprising collecting bacterial growth data in samples at a fixed time; identifying and processing a jump point in the bacterial growth data; fitting effective bacterial growth data to obtain a fitting function; and judging sample positivity for the slow-growing bacteria by judging constants c and b in the fitting function, fitting goodness, a logarithmic phase point number and a plateau phase point number, and a lower convex value. The method significantly improves the positive detection rate and effectively reduces the false positive problem caused by the rapid increase of samples in the early stage.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of bacteria identification, and in particular to a method for identifying slow-growing bacteria. BACKGROUND

[0002] Blood culture is a kind of artificial culture method that inoculates fresh ex vivo blood samples on nutrient medium under certain temperature, humidity and other conditions, so that bacteria with higher nutritional requirements grow and reproduce, and then the pathogenic bacteria are identified to determine the pathogenic bacteria. When pathogenic bacteria are detected in blood culture, the sample is positive; when no pathogenic bacteria are detected in blood culture, the sample is negative. However, due to the characteristics of slow-growing bacteria in blood culture, long growth duration, S-shaped growth curve and poor data quality, the identification of bacteria in the blood culture process is directly affected, and false positives and false negatives often occur in the sample, which makes the commonly used bacteria identification methods not suitable for identifying slow-growing bacteria. SUMMARY

[0003] The present application relates to the field of bacteria identification, and in particular to a method for identifying slow-growing bacteria.

[0004] To achieve the above-mentioned purpose, the present application can adopt the following technical solutions:

[0005] The method for identifying slow-growing bacteria according to the present application comprises the following steps:

[0006] S1, collecting bacteria growth data in the sample at regular intervals;

[0007] S2, discarding the bacteria growth data in the sample at the beginning stage, and identifying and processing the jump points in the bacteria growth data to generate valid bacteria growth data;

[0008] S3, determining whether the number of valid bacteria growth data meets a preset minimum value; when the preset minimum value is met, performing S4 step, otherwise terminating the identification;

[0009] S4, fitting the valid bacteria growth data to obtain a fitting function;

[0010] S5, determining whether the constant c in the fitting function is less than a detection threshold and whether the constant b is greater than a slope threshold; when the constant c is less than the detection threshold and the constant b is greater than the slope threshold, performing S6 step, otherwise performing S4 step;

[0011] S6, determining whether the goodness of fit R 2 is greater than a first threshold; when the goodness of fit R 2 is greater than the first threshold, performing S7 step; otherwise, performing S4 step;

[0012] S7, in combination with the collected bacterial growth data, checking the number of logarithmic phase points and the number of plateau phase points, if the number of logarithmic phase points meets the first logarithmic phase point number threshold value, and the number of plateau phase points meets the plateau phase point number threshold value, performing S8 step, otherwise performing S9 step;

[0013] S8, calculating the lower convex value, and judging whether the lower convex value is greater than the first concave-convex threshold value; when the lower convex value is greater than the first concave-convex threshold value, the sample is displayed as positive; otherwise, performing S9 step;

[0014] S9, judging whether the goodness of fit R 2 is greater than the second threshold value; when the goodness of fit R 2 is greater than the second threshold value, performing S10 step; otherwise, performing S4 step;

[0015] S10, in combination with the collected bacterial growth data, checking the number of logarithmic phase points; if the number of logarithmic phase points meets the second logarithmic phase point number threshold value, performing S11 step, otherwise performing S4 step;

[0016] S11, calculating the lower convex value, and judging whether the lower convex value is greater than the second concave-convex threshold value; when the lower convex value is greater than the second concave-convex threshold value, the sample is displayed as positive; otherwise, performing S4 step.

[0017] Further, in S2 step, the identifying and processing the jump point in the bacterial growth data comprises the following steps:

[0018] S2.1, calculating the first-order difference of each sampling time; the first-order difference refers to the difference between the bacterial growth data at the later sampling time and the bacterial growth data at the previous sampling time of the continuous two sampling times;

[0019] S2.2, calculating the first-order difference sliding mean value at each sampling time; the first-order difference sliding mean value refers to the arithmetic mean value of the continuous multiple first-order differences;

[0020] S2.3, finding out the jump point; the jump point refers to that the absolute value of the first-order difference at the current sampling time is continuously greater than the product of each first-order difference sliding mean value within a preset step length before the current sampling time and a preset multiple;

[0021] S2.4, subtracting the jump value from the jump point and the bacterial growth data collected after the jump point, and adding the previous first-order difference sliding mean value, to generate the effective bacterial growth data.

[0022] Further, in S4 step, the following steps are included:

[0023] S4.1, when performing for the first time, performing data fitting on all the effective bacterial growth data to obtain the fitting function;

[0024] S4.2, then each time execution, on the basis of the previous participation in the fitting of the effective bacterial growth data, discard sampling time in front of the effective bacterial growth data, and with the preset step size equal to the number of remaining effective bacterial growth data, data fitting, obtain the fitting function;

[0025] The data fitting includes L-M algorithm, Newton method, quasi-Newton method; the fitting function is Wherein a, b, c, d, e, f, g are constants, and x represents the sampling time.

[0026] Further, in the step S6, the bacterial growth data on the sampling time and the bacterial growth prediction data calculated by the fitting function in the step S4 are calculated to calculate the fitting degree R 2 , R 2 Is greater than 0 and less than 1, the greater the value of R 2 The fitting degree is also high.

[0027] Further, in the step S8, the lower convex value calculation formula is: Wherein Take 0.5, And The fitting function Starts from the sampling time and the last sampling time.

[0028] The present application has the advantages that for bacterial growth slow sample, the positive detection rate can be significantly improved, and the false positive problem caused by the sample increasing faster in the early stage is effectively reduced. BRIEF DESCRIPTION OF DRAWINGS

[0029] Figure 1 Is the flow chart of the method of the present application.

[0030] Figure 2 Is the bacterial growth curve diagram drawn by the method of the present application.

[0031] Figure 3 Is the jump point identification schematic diagram of the method of the present application.

[0032] Figure 4 Is the effective growth curve diagram drawn by the method of the present application.

[0033] Figure 5 Is the fitting function value process schematic diagram of the method of the present application.

[0034] Figure 6 Is the lower convex value calculation schematic diagram of the method of the present application. DETAILED DESCRIPTION

[0035] The technical solutions in the embodiments of the present application will be clearly and completely described below. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the present application.

[0036] As shown in Figure 1 The method for identifying slow-growing bacteria according to the present application comprises the following steps:

[0037] S1, collecting the bacterial growth data in the sample at regular time intervals;

[0038] The bacterial growth data collected at regular time intervals can be plotted into a bacterial growth curve, as shown in Figure 2 The horizontal axis represents the sampling time, and the vertical axis represents the fluorescence value. The bacteria grow in the culture bottle to produce CO2, which activates the fluorescent substance embedded at the bottom of the culture bottle. The fluorescent substance is excited to emit fluorescence under the irradiation of the light-emitting diode. The fluorescence detector at the bottom of the culture hole detects the fluorescence and transmits the data to the microprocessor. The microprocessor calculates the fluorescence value through a series of preset operation modes. The fluorescence value is proportional to the CO2 concentration, and the CO2 concentration is proportional to the microbial concentration. The fluorescence intensity is associated with the microbial concentration through CO2. The fluorescence value reflects the growth data of the microorganism. The greater the fluorescence value, the higher the microbial growth concentration.

[0039] S2, discarding the bacterial growth data in the sample at the beginning stage, and identifying and processing the jump point in the bacterial growth data to generate effective bacterial growth data;

[0040] At the beginning stage of blood culture, if the bacterial growth data continuously decreases, the bacterial growth data is discarded until the bacterial growth data increases. Then, the stage of identifying and processing the jump point in the bacterial growth data is entered, which specifically comprises the following steps:

[0041] S2.1, calculating the first-order difference at each sampling time; the first-order difference refers to the difference between the bacterial growth data at the later sampling time and the bacterial growth data at the previous sampling time at two consecutive sampling times;

[0042] S2.2, calculating the first-order difference sliding average at each sampling time; the first-order difference sliding average refers to the arithmetic mean of a plurality of first-order differences;

[0043] For example, the arithmetic mean of three consecutive first-order differences is calculated to obtain the first-order difference sliding average. Then, the arithmetic mean of the first-order difference at the current sampling time and the two consecutive first-order differences before the current sampling time is calculated to obtain the first-order difference sliding average at the current sampling time.

[0044] S2.3, Find the jump point; the jump point is the product of the moving average of each first difference and a preset multiple within a preset step size before the absolute value of the first difference at the current sampling time is continuously greater than the current sampling time.

[0045] like Figure 3 As shown in the chart, the first column represents bacterial growth data at the sampling time, the second column represents the absolute value of the first-order difference at the sampling time, the third column represents the first-order difference at the sampling time, and the fourth column represents the moving average of the first-order difference at the sampling time. When searching for jump points, a preset step size and a preset multiplier are first determined, such as a preset step size of 10 and a preset multiplier of 5. It can be seen that when the bacterial growth data is 1965, its absolute value of the first-order difference is 16, which is continuously greater than the moving average of the first-order differences for the 10 samples prior to its sampling time, and is more than 5 times greater than each of the moving averages. Therefore, this sampling point is a jump point, and the absolute value of the first-order difference at this point is the jump value.

[0046] S2.4 The effective bacterial growth data is generated by subtracting the jump value from the bacterial growth data collected at the jump point and after the jump point, and adding the previous first-order difference moving average.

[0047] like Figure 4 As shown, the effective bacterial growth data are plotted as a curve according to the sampling time, with the horizontal axis representing the sampling time and the vertical axis representing the fluorescence value after processing.

[0048] S3, determine whether the number of valid bacterial growth data meets the preset minimum value; if the preset minimum value is met, proceed to step S4, otherwise terminate the identification.

[0049] S4, Fit the effective bacterial growth data to obtain the fitting function; specifically including the following steps:

[0050] S4.1, During the first execution, perform data fitting on all the effective bacterial growth data to obtain the fitting function;

[0051] S4.2, In each subsequent execution, based on the effective bacterial growth data that participated in the fitting in the previous time, discard the effective bacterial growth data that was sampled earlier and whose number is equal to the preset step size, and perform data fitting on the remaining effective bacterial growth data to obtain the fitting function; when the number of effective data participating in the fitting is less than the preset step size, the identification is terminated.

[0052] The data fitting includes the LM algorithm, Newton's method, and quasi-Newton method; the fitting function is... , where a, b, c, d, e, f, g are constants, and x represents the sampling time.

[0053] like Figure 5As shown, one sample collects 31 growth data, and the preset step is 10, so the first 31 growth data are all involved in fitting, and after obtaining the fitting function, the sample positive judgment is performed according to S5-S11. When the second cycle executes S4, on the basis of the 31 effective growth data involved in fitting in the previous time, 10 effective growth data are discarded, which are collected in the previous time and equal to the preset step, and the remaining 21 effective growth data are fitted to obtain the fitting function;

[0054] When the third cycle executes S4, on the basis of the 21 effective growth data involved in fitting in the previous time, 10 effective growth data are discarded, which are collected in the previous time and equal to the preset step, and the remaining 11 effective growth data are fitted to obtain the fitting function; when the number of effective data involved in fitting is less than the preset step, the identification is terminated;

[0055] S5, judge whether the constant c in the fitting function is less than the detection threshold and whether the constant b is greater than the slope threshold; when the constant c is less than the detection threshold and the constant b is greater than the slope threshold, execute S6, otherwise execute S4;

[0056] S6, judge the goodness of fit R 2 of the fitting function; when the goodness of fit R 2 is greater than the first threshold, execute S7; otherwise, execute S4;

[0057] According to the bacterial growth data on the sampling time and the bacterial growth prediction data calculated by the fitting function in S4, the goodness of fit R 2 is calculated; when the goodness of fit R 2 is greater than 0 and less than 1, the value of R 2 is greater, which represents the goodness of fit is also high.

[0058] S7, combined with the collected bacterial growth data, check the number of logarithmic phase points and the number of plateau points; if the number of logarithmic phase points meets the first logarithmic phase point threshold and the number of plateau points meets the plateau point threshold, execute S8, otherwise execute S9;

[0059] S8, calculate the concave value and judge whether the concave value is greater than the first concave-convex threshold; when the concave value is greater than the first concave-convex threshold, display the sample as positive; otherwise, execute S9;

[0060] The concave value calculation formula is: , wherein 0.5, and are the fitting function starting from the sampling time and the last sampling time.

[0061] S9, judging the goodness of fit R 2 whether greater than a second threshold value; when the goodness of fit R 2 is greater than the second threshold value, performing S10 step; otherwise, performing S4 step;

[0062] As Figure 6 shown in the figure, where the curve 1 composed of small blocks represents the bacterial growth curve, and the curve 2 is the fitting function corresponding to the bacterial growth curve, according to the lower convex value calculation formula, taking x3 is the fitting function the starting sampling point corresponding to the sampling time, that is, the left end point of the curve 2, x4 is the fitting function the terminal sampling point corresponding to the sampling time, that is, the right end point of the curve 2.

[0063] S10, combining the collected bacterial growth data, checking the number of logarithmic phase points; if the number of logarithmic phase points meets the second logarithmic phase point threshold value, performing S11 step, otherwise, performing S4 step;

[0064] S11, calculating the lower convex value, and judging whether the lower convex value is greater than the second concave-convex threshold value; when the lower convex value is greater than the second concave-convex threshold value, displaying the sample as positive; otherwise, performing S4 step.

Claims

1. A method for identifying slow-growing bacteria, characterized in that: Includes the following steps: S1, periodically collect bacterial growth data from the sample; S2, discard bacterial growth data that shows a continuous decline in bacterial growth data at the beginning of the sample, and identify and process jump points in bacterial growth data to generate effective bacterial growth data; The identification and processing of the jump points in the bacterial growth data includes the following steps: S2.1, Calculate the first-order difference for each sampling time; the first-order difference refers to the difference between the bacterial growth data at the later sampling time and the bacterial growth data at the previous sampling time in two consecutive sampling times. S2.2, Calculate the first-order difference moving average at each sampling time; the first-order difference moving average refers to the arithmetic mean of multiple consecutive first-order differences; S2.3, Find the jump point; the jump point is the product of the moving average of each first difference and a preset multiple within a preset step size before the absolute value of the first difference at the current sampling time is continuously greater than the current sampling time. S2.4, The effective bacterial growth data is generated by subtracting the jump value from the bacterial growth data collected at and after the jump point, and adding the previous first-order difference moving average; the jump value is the absolute value of the first-order difference at the jump point. S3, determine whether the number of valid bacterial growth data meets the preset minimum value; if the preset minimum value is met, proceed to step S4, otherwise terminate the identification. S4. Perform sliding sampling fitting on the effective bacterial growth data to obtain the fitting function; Specifically: S4.1, during the first execution, perform data fitting on all the effective bacterial growth data to obtain the fitting function; S4.2, In each subsequent execution, based on the effective bacterial growth data that participated in the fitting in the previous time, discard the effective bacterial growth data that was sampled earlier and whose number was equal to the preset step size, and perform data fitting on the remaining effective bacterial growth data to obtain the fitting function. The data fitting includes the LM algorithm, Newton's method, and quasi-Newton method; the fitting function is... , where a, b, c, d, e, f, g are constants, and x represents the sampling time; S5, determine whether the constant c in the fitting function is less than the detection threshold and whether the constant b is greater than the slope threshold. If the constant c is less than the detection threshold and the constant b is greater than the slope threshold, proceed to step S6; otherwise, proceed to step S4. S6, Determine the goodness of fit R 2 Whether it is greater than the first threshold; when the goodness of fit R 2 If the value is greater than the first threshold, proceed to step S7; otherwise, proceed to step S4. S7. Based on the collected bacterial growth data, check the number of logarithmic phase points and the number of plateau phase points. If the number of logarithmic phase points meets the first logarithmic phase point threshold and the number of plateau phase points meets the plateau phase point threshold, proceed to step S8; otherwise, proceed to step S9. S8, calculate the convexity value and determine whether the convexity value is greater than the first concavity / convexity threshold; if the convexity value is greater than the first concavity / convexity threshold, the sample is displayed as positive; otherwise, proceed to step S9; the formula for calculating the convexity value is: ,in Take 0.5, and The fitting function The initial sampling time and the last sampling time; S9, Determine the goodness of fit R 2 Is it greater than the second threshold? When the goodness of fit R 2 If the value is greater than the second threshold, proceed to step S10; otherwise, proceed to step S4. S10, Based on the collected bacterial growth data, check the number of logarithmic phase points; if the number of logarithmic phase points meets the second logarithmic phase point threshold, proceed to step S11; otherwise, proceed to step S4. S11, calculate the lower convexity value and determine whether the lower convexity value is greater than the second concavity threshold; if the lower convexity value is greater than the second concavity threshold, the sample is displayed as positive; otherwise, proceed to step S4.

2. The method for identifying slow-growing bacteria according to claim 1, characterized in that: In step S6, the goodness of fit R is calculated based on the bacterial growth data at the sampling time and the bacterial growth prediction data calculated by the fitting function in step S4. 2 R 2 R is greater than 0 and less than 1. 2 A higher value indicates a higher degree of fit.

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