Method for distinguishing Gram positive and negative properties

Abstract

The invention relates to the technical field of microbiology, in particular to a method for distinguishing gram negative and positive properties, which greatly shortens the detection time through automatic optical detection and data analysis, so that gram typing of bacteria can be completed in a shorter time, and the detection efficiency is improved. Through accurate optical signal acquisition and an advanced data processing technology, gram-positive bacteria and gram-negative bacteria can be accurately distinguished, personal errors are reduced, and the accuracy of a detection result is improved. The Gram typing step is simplified, an operator does not need a complex dyeing technology and a microscope operation skill, and the dependence on professionals is reduced. The optical characteristic difference of bacteria is visually displayed in the form of histograms and scatter diagrams, so that the result is easier to understand and explain. Therefore, the problem that a traditional gram staining method is low in efficiency is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of microbiology, and particularly to a method for discriminating Gram-positive and Gram-negative bacteria. Background Art

[0002] Gram Staining is a method used to identify bacteria: This staining method utilizes the different biochemical properties on the bacterial cell wall to divide bacteria into two categories, namely Gram Positive and Gram Negative. This staining method was invented by the Danish doctor Hans Christian Gram in 1884. Initially, it was used to distinguish the relationship between Streptococcus pneumoniae and Klebsiella pneumoniae, and later it was extended as one of the important characteristics for identifying bacterial species, and has a wide range of uses in the clinical diagnosis and treatment of diseases caused by bacterial infections. This classification is of great significance for understanding the physiological characteristics, pathogenic mechanisms of bacteria and guiding the selection of clinical antibiotics.

[0003] Although the Gram staining method plays a key role in microbial identification, its operation process is complex and requires multiple steps, including smearing, fixing, staining, decolorizing and counterstaining. The whole process is time-consuming and requires high skills of the operator, and is easily affected by human factors, resulting in limited repeatability and accuracy of the results. In addition, the traditional Gram staining method cannot achieve high-throughput analysis, which is an obvious shortcoming for clinical and scientific research environments that need to quickly process a large number of samples. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for discriminating Gram-positive and Gram-negative bacteria, aiming to solve the problem of low efficiency of the traditional Gram staining method.

[0005] To achieve the above purpose, the present invention provides a method for discriminating Gram-positive and Gram-negative bacteria, including the following steps:

[0006] Obtain the morphological characteristics of bacteria in the sample by using planar sheath flow digital imaging technology;

[0007] Obtain the optical signals of bacteria in the sample by using fluorescence flow cytometry;

[0008] Convert the optical signals into quantitative parameters;

[0009] Classify the bacteria by using an intelligent algorithm according to the morphological characteristics and quantitative parameters, and determine whether it is Gram-positive or Gram-negative.

[0010] Wherein, in the step of "obtaining the morphological characteristics of bacteria in the sample by using planar sheath flow digital imaging technology", the following steps are included:

[0011] Select a sheath flow liquid containing sodium chloride and surfactant to wrap the sample;

[0012] Use a high-resolution microscope and a CCD camera to capture images of bacteria in the sample;

[0013] Extract the morphological characteristics of bacteria through an image processing algorithm.

[0014] Among them, the morphological characteristics of the bacteria include size, shape, texture, transmittance, and reflectivity parameters.

[0015] Among them, the quantitative parameters include the starting position, peak position, trough position, waveform height, waveform width, and waveform area of the waveform.

[0016] Among them, in "obtaining the optical signal of bacteria in the sample through fluorescence flow cytometry", the following steps are included:

[0017] Mix the sample with a diluent and a staining solution to prepare a test sample;

[0018] Form a single-particle flow of the sample under a certain pressure through a sheath flow device;

[0019] Irradiate the sample with a red laser beam and collect forward scattered light, side scattered light, and fluorescence pulse signals.

[0020] A method for discriminating Gram-positive and Gram-negative bacteria according to the present invention includes the following steps: obtaining the morphological characteristics of bacteria in the sample by using planar sheath flow digital imaging technology; obtaining the optical signal of bacteria in the sample by fluorescence flow cytometry; converting the optical signal into quantitative parameters; classifying the bacteria through an intelligent algorithm according to the morphological characteristics and quantitative parameters, and determining whether it is Gram-positive or Gram-negative. Through automated optical detection and data analysis, the present invention greatly shortens the detection time, enabling the Gram typing of bacteria to be completed in a shorter time and improving the detection efficiency. Through precise optical signal acquisition and advanced data processing technology, Gram-positive bacteria and Gram-negative bacteria can be accurately distinguished, reducing human error and improving the accuracy of detection results. The steps of Gram typing are simplified, and operators do not need complex staining techniques and microscope operation skills, reducing the dependence on professionals. The optical property differences of bacteria are intuitively displayed in the form of histograms and scatter plots, making the results easier to understand and interpret. Thus, the problem of low efficiency of traditional Gram staining methods is solved. Description of the Drawings

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the accompanying drawings required for use in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0022] Figure 1 It is a flowchart of a method for discriminating Gram-positive and Gram-negative provided by the present invention.

[0023] Figure 2 It is a flowchart of obtaining the morphological characteristics of bacteria in a sample using planar sheath flow digital imaging technology.

[0024] Figure 3 It is a flowchart of obtaining the optical signal of bacteria in a sample through fluorescence flow cytometry.

[0025] Figure 4 It is an example scatter plot of bacteria in a sample obtained through fluorescence flow cytometry.

[0026] Figure 5 It is an example diagram of region A, which is the region where the fluorescence intensity is less than the threshold k.

[0027] Figure 6 It is an example diagram of region B, which is mainly the region where the bacteria points are located.

[0028] Figure 7 It is an example diagram of region C, which is mainly the region where the bacteria points are located, and almost excludes the corresponding points of all non-bacterial components.

[0029] Figure 8 It is an example diagram of subdividing region C for judging Gram typing. Detailed implementation manners

[0030] The following will describe in detail the embodiments of the present invention. The examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals represent the same or similar elements or elements with the same or similar functions from beginning to end. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the present invention, but should not be construed as a limitation to the present invention.

[0031] Please refer to Figures 1 to 8 , the present invention provides a method for discriminating Gram-positive and Gram-negative, including the following steps:

[0032] S1 Obtain the morphological characteristics of bacteria in the sample using planar sheath flow digital imaging technology;

[0033] S11 Select a sheath fluid containing sodium chloride and a surfactant to wrap the sample;

[0034] Specifically, the planar sheath flow digital imaging technology is an advanced imaging technology used to identify and analyze the formed elements in liquid samples. A sheath flow liquid containing sodium chloride and surfactant is selected to wrap the specimen to ensure the stability of the sample in the flow channel. A planar flow channel is designed to ensure the uniform distribution of the sample in the channel and avoid sample aggregation or precipitation. An accurate pump system is used to control the flow rates of the sheath flow liquid and the specimen. The flow rates are adjusted to ensure that the formed elements have a long enough residence time in the imaging area for high-resolution imaging.

[0035] S12 Use a high-resolution microscope and a CCD camera to capture images of bacteria in the specimen;

[0036] Specifically, select a highly sensitive 4-choice CCD camera to capture images of the formed elements in the specimen. Select a 0x high-resolution microscope to observe the formed elements in the specimen in order to obtain high-resolution images.

[0037] S13 Extract the morphological characteristics of bacteria through image processing algorithms.

[0038] The morphological characteristics of the bacteria include parameters such as size, shape, texture, transmittance, and reflectance.

[0039] Specifically, a high-performance image processor is selected for real-time processing and analysis of the captured images. The images of the formed elements in the specimen are captured in real time through a microscope and a camera. Ensure that the resolution of the images is high enough to accurately identify and analyze the morphological characteristics of the formed elements. The captured images are stored in a high-performance storage device for subsequent analysis and processing. Image enhancement algorithms are used for contrast adjustment and gray-scale transformation to improve the image quality. Filtering algorithms such as Gaussian filtering and mean filtering are used to remove noise from the images. Threshold segmentation algorithms are used to segment the images into foreground and background, and the contours of the formed elements are extracted. Edge detection algorithms are used to detect the edges of the formed elements. Morphological feature extraction algorithms are used to extract the size, shape, and edge features of the formed elements. Texture feature extraction algorithms such as gray-level co-occurrence matrix and local binary pattern (LBP) are used to extract the texture features of the formed elements. Morphological feature extraction algorithms are used to extract the size, circularity, and aspect ratio of the formed elements. Texture feature extraction algorithms are used to extract the texture uniformity and texture direction of the formed elements. Optical feature extraction algorithms are used to extract the transmittance and reflectance of the formed elements. Multiple features are fused to form a comprehensive feature vector of the formed elements. A suitable neural network (CNN) is used to input the feature vector to form an identification model for identifying the formed elements, and the identification parameters of the specimen are output. The bacterial morphology in the specimen can be identified, which may be cocci, short bacilli, long bacilli, streptococci, etc. Bacilli are bacteria with a rod-shaped or cylindrical morphology. Although their sizes vary widely, generally the short diameter is 0.2-1 micrometer, and the long diameter is 1-5 micrometers. Cocci are bacteria with a spherical morphology. Cocci include streptococci and staphylococci. Streptococci are cocci with a diameter of 1 micrometer, and each bacterium is regularly arranged in a straight chain. Staphylococci are cocci with a diameter of about 1 micrometer, and each bacterium is irregularly arranged like a bunch of grapes.

[0040] S2 Obtain the optical signals of the bacteria in the specimen through fluorescence flow cytometry;

[0041] S21 Mix the specimen with a diluent and a staining solution to prepare a test specimen;

[0042] Specifically, fluorescence flow cytometry can quantitatively obtain various parameter information of the particles in the suspension. Mix the mixed specimen, diluent, and staining solution to prepare a test specimen.

[0043] S22 Form a single-particle stream of the specimen under a certain pressure through a sheath flow device;

[0044] Specifically, the collected sample is mixed with a diluent and a staining solution to prepare a test sample. The diluent is selected based on its ability to reduce the viscosity of the sample and disperse the bacteria. The staining solution, according to its interaction with the bacterial components, is commonly polymethine and the like. The prepared test sample is passed through a sheath flow cell under a certain pressure to form a single-particle stream wrapped by a sheath fluid. The design of the sheath flow cell ensures that the bacteria in the sample are arranged in the form of single particles, avoiding the interaction between particles.

[0045] S23 The sample is irradiated with a red laser beam to collect forward scatter light, side scatter light, and fluorescence pulse signals.

[0046] Specifically, the particles are irradiated with a red laser beam of a specific wavelength emitted by a laser. The selection of the laser beam is based on its ability to excite the optical response after bacterial staining, so as to obtain the best scatter light and fluorescence signals. A highly sensitive photodetector is used to accurately capture the forward scatter light, side scatter light, and fluorescence pulse signals and convert them into electrical signals for subsequent analysis. These signal information directly reflects the physical and chemical properties of the bacteria and is an important basis for discriminating Gram typing.

[0047] S3 Convert the optical signal into a quantitative parameter;

[0048] The quantitative parameter includes the starting position, peak position, trough position, waveform height, waveform width, and waveform area of the waveform.

[0049] Specifically, after a series of circuits such as photoelectric conversion, operational amplifier, and ADC sampling, the optical signal will carry noise, such as power supply noise, thermal noise, ADC sampling noise, etc., and it needs to be filtered, otherwise it will affect waveform recognition and calculation. Through simulation testing of the physical waveform samples, it is found that using an FIR filter can effectively filter out the noise; different urine samples will have different bases for the optical signals after sampling. The waveform values within a certain time sequence range are sorted and statistically analyzed in real time, and the flat area value is calculated using the standard deviation as the base value; because filtering has been done before, the sawteeth and burrs of the waveform have been effectively filtered out, so it is very easy to determine the rising edge of the waveform using the gradient method. The gradient threshold is adjustable, and the edge detection sensitivity can be adjusted according to needs. When the three gradient values of four consecutive sampling points are all greater than the set threshold and the value of the fourth sampling point is greater than the base value, this sampling point is the starting point of the optical signal waveform of the particle to be measured. When the subsequent waveform value is less than the value of this sampling point, it represents the end of the particle waveform; through edge detection, the complete waveform of the particle optical signal and its starting position can be known. The total number of sampling points from the start to the end is the waveform width; the sum of the values of all sampling points from the start to the end is the waveform area; the maximum value of the complete waveform values is statistically analyzed in the order of the time sequence, and its corresponding position is the peak position; subtracting the base value from the maximum value is the waveform height; if there are multiple peaks in a complete waveform, the gradient between the peaks will change from a negative gradient value to a positive gradient value, and this is the valley position. Using an advanced pulse recognition algorithm, key parameters are extracted from the collected optical signals, including the starting position, peak position, valley position, waveform height, waveform width, waveform area, etc. of the waveform. These parameters are quantitative indicators describing the optical characteristics of bacteria, and their changes are directly related to the Gram classification of bacteria.

[0050] S4 Classify the bacteria through an intelligent algorithm based on the morphological characteristics and quantitative parameters, and determine whether it is Gram-positive or Gram-negative.

[0051] Specifically, a two-dimensional scatter plot is made with the forward scatter light intensity and fluorescence intensity. The horizontal axis is the fluorescence intensity, and the vertical axis is the forward intensity. Here, each bacterium is located at the specified position on the two-dimensional scatter plot according to its forward scatter light intensity and lateral fluorescence intensity. The area where the data points with fluorescence intensity less than the threshold k is taken as area A, where k is an arbitrary value consistent with the fluorescence intensity unit, generally greater than 0. To the left of the straight line a in the figure is area A, which is mainly the area where the scatter points of bacteria and non-bacterial components in the sample are located. Area B is the area where the bacterial data points are located. By taking the origin O of the scatter plot as the origin and making a straight line b with an angle θ with the horizontal axis, where θ represents the angle and is in the range of 0° to 90°, the area between the straight line b and the horizontal axis is obtained as area B, and the bacterial data points are mainly distributed in this area. After excluding the intersection part of area A and area B, area C is obtained. Area C excludes non-bacterial data, thereby improving the accuracy of bacterial Gram classification.

[0052] The difference in FL_P corresponding to the maximum fluorescence intensity and region A is evenly divided into Q parts, where Q can be any value greater than 1, such as 2, 3, 4,..., for better illustration, in this article, it is taken as an example of being evenly divided into 3 parts. At the corresponding points, straight lines perpendicular to the FL_P axis are drawn, which are straight lines c, d, e,..., and at the intersections of straight lines a, c, d, e,... and straight line b, straight lines parallel to the FL_P axis are drawn, which are straight lines f, g, h,..., and region C is divided into regions C1, C2, C3, C4, C5, C6, C7, C8, C9,....

[0053]

[0053]

[0054] The above-disclosed is only a preferred embodiment of a method for discriminating Gram-positive and Gram-negative in the present invention. Of course, it cannot be used to limit the scope of the rights of the present invention. Those of ordinary skill in the art can understand all or part of the processes of implementing the above embodiments, and the equivalent changes made according to the claims of the present invention still fall within the scope covered by the invention.

Claims

1. A method for determining Gram positive or negative, characterized in that: The following steps are involved: The planar sheath flow digital imaging technique is used to obtain the morphological characteristics of bacteria in the sample; Obtain the optical signal of bacteria in the sample by fluorescence flow cytometry; converting the optical signal into a quantitative parameter; Based on the morphological characteristics and quantitative parameters, the bacteria are classified by an intelligent algorithm to determine whether they are Gram-positive or Gram-negative.

2. The method for determining Gram positive or negative according to claim 1, wherein: In "obtaining morphological characteristics of bacteria in a sample using planar sheath flow digital imaging technology", the following steps are included: Select a sheath fluid containing sodium chloride and a surfactant to wrap the sample; Use a high-resolution microscope and CCD camera to capture images of bacteria in the specimen; The morphological features of bacteria are extracted through image processing algorithms.

3. The method for determining Gram positive or negative according to claim 2, wherein: The morphological characteristics of the bacteria include size, shape, texture, transmittance and reflectance parameters.

4. The method for determining Gram positive or negative according to claim 1, wherein: The quantitative parameters include the starting position, peak position, trough position, waveform height, waveform width and waveform area of ​​the waveform.

5. The method for determining Gram positive or negative according to claim 1, wherein: In "obtaining optical signals of bacteria in a sample by fluorescence flow cytometry", the following steps are included: Mixing the test sample with the diluent and the staining solution to prepare a test sample; The sample is formed into a single particle flow under a certain pressure through a sheath flow device; The sample is illuminated by a red laser beam, and forward scattered light, side scattered light and fluorescence pulse signals are collected.

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